Chest diseases if not properly diagnosed in early stages can be fatal. Because of lack of skilled
knowledge or experiences of real life practitioners, many a times one chest disease is wrongly diagnosed for the
other, which leads to wrong treatment. Due to this the actual disease keeps on growing and become fatal. For
example, muscular chest pains can be treated for the heart disease or COPD is treated for Asthma. Early
prediction of chest disease is crucial but is not an easy task. Consequently, the computer based prediction system
for chest disease may play a significant role as a pre-stage detection to take proper actions with a view to recover
from it. However the choice of the proper Data Mining classification method can effectively predict the early
stage of the disease for being cured from it. In this paper, the three mostly used classification techniques such as
support vector machine (SVM), k-nearest neighbour (KNN) and artificial neural network (ANN) have been studied
with a view to evaluating them for chest disease prediction.
IRJET - Machine Learning for Diagnosis of Diabetes
This document describes a study that uses machine learning models to predict whether a person has diabetes based on patient data. The researchers created several classification models using algorithms like logistic regression and support vector machines on a diabetes dataset. The models with the highest accuracy at predicting diabetes were random forest and gradient boosting. An Android app was also developed to input patient data, run the predictions from the trained models, and display the results to help diagnose diabetes. The goal is to help reduce diabetes rates and healthcare costs by improving diagnosis.
Various Data Mining Techniques for Diabetes Prognosis: A Review
Most of the food we eat is converted to glucose, or sugar which is used for energy. When you have diabetes, your body either doesnt make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in your blood leading to complications like heart disease, stroke, neuropathy, poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Data mining adopts a series of pattern recognition technologies and statistical and mathematical techniques to discover the possible rules or relationships that govern the data in the databases. Data mining plays an important role in data prediction. There are different types of diseases predicted in data mining namely Hepatitis, Lung Cancer, Liver disorder, Breast cancer, Thyroid disease, Diabetes etc¦ This paper analyzes the Diabetes predictions. Misba Reyaz | Gagan Dhawan"Various Data Mining Techniques for Diabetes Prognosis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12927.pdf http://www.ijtsrd.com/engineering/computer-engineering/12927/various-data-mining-techniques-for-diabetes-prognosis-a-review/misba-reyaz
IRJET- Cancer Disease Prediction using Machine Learning over Big Data
1. The document discusses using machine learning algorithms to predict cancer and other diseases by analyzing big healthcare data. It specifically looks at using support vector machines (SVM) for cancer prediction and classification.
2. SVM is presented as a powerful machine learning tool for cancer classification and identification of biomarkers, drug targets, and cancer-driving genes. The paper also examines applying machine learning algorithms like SVM to predict outbreaks of chronic diseases in populations using medical data.
3. The researchers aim to improve disease prediction accuracy by addressing issues with incomplete or inconsistent medical data from different regions. They also seek to enable early diagnosis and treatment by analyzing large healthcare datasets with machine learning.
Prediction of Diabetes using Probability ApproachIRJET Journal
This document discusses using a Bayesian Network classifier to predict whether individuals have diabetes based on various attributes. It analyzes a Pima Indian Diabetes dataset containing information on individuals with and without diabetes. The study aims to help identify diabetes and improve people's lifestyles by making them aware of the disease and how to treat it. It evaluates the prediction performance of Bayesian algorithms for classifying individuals as diabetic or non-diabetic.
(Paper) Emergency Medical Support System for Visualizing Locations and Vital ...Naoki Shibata
The triage tag is used in Mass Casualty Incident (MCI) to check the priority of patients treatments and conditions. However, it is difficult to grasp a change in the patient’s information since it is a paper tag. In this paper, we propose a system using the electronic triage tag (eTriage) that facilitates emergency medical technicians to grasp patients locations and conditions through visualization. This system provides the following three views of the patients information: (1) Inter-site view which shows on a map an overview of the latest status in multiple first-aid stations including the number of technicians and patients of each triage category; (2) Intra-site view which shows detailed status of each first-aid station including the location, triage category, and vital signs of each patient on a 3D map created based on the environment mapping technique; and (3) Individual view which shows vital information of patients on a tablet PC according to its orientation using the augmented reality technique. In this paper, we describe the design and implementation of the proposed system with some preliminary evaluation results.
IRJET- Diabetes Prediction using Machine LearningIRJET Journal
This document discusses predicting diabetes using machine learning algorithms. It analyzes the Pima Indian diabetes dataset using Support Vector Machine (SVM), K-Nearest Neighbors, and Decision Tree algorithms. SVM achieved the highest accuracy of 80% for predicting whether a patient has diabetes. Key features like glucose level and body mass index were most important for prediction. A GUI was created to allow users to enter patient data and predict diabetes status using the SVM model trained on the dataset.
IRJET - Machine Learning for Diagnosis of DiabetesIRJET Journal
This document describes a study that uses machine learning models to predict whether a person has diabetes based on patient data. The researchers created several classification models using algorithms like logistic regression and support vector machines on a diabetes dataset. The models with the highest accuracy at predicting diabetes were random forest and gradient boosting. An Android app was also developed to input patient data, run the predictions from the trained models, and display the results to help diagnose diabetes. The goal is to help reduce diabetes rates and healthcare costs by improving diagnosis.
Various Data Mining Techniques for Diabetes Prognosis: A Reviewijtsrd
Most of the food we eat is converted to glucose, or sugar which is used for energy. When you have diabetes, your body either doesnt make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in your blood leading to complications like heart disease, stroke, neuropathy, poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Data mining adopts a series of pattern recognition technologies and statistical and mathematical techniques to discover the possible rules or relationships that govern the data in the databases. Data mining plays an important role in data prediction. There are different types of diseases predicted in data mining namely Hepatitis, Lung Cancer, Liver disorder, Breast cancer, Thyroid disease, Diabetes etc¦ This paper analyzes the Diabetes predictions. Misba Reyaz | Gagan Dhawan"Various Data Mining Techniques for Diabetes Prognosis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12927.pdf http://www.ijtsrd.com/engineering/computer-engineering/12927/various-data-mining-techniques-for-diabetes-prognosis-a-review/misba-reyaz
IRJET- Cancer Disease Prediction using Machine Learning over Big DataIRJET Journal
1. The document discusses using machine learning algorithms to predict cancer and other diseases by analyzing big healthcare data. It specifically looks at using support vector machines (SVM) for cancer prediction and classification.
2. SVM is presented as a powerful machine learning tool for cancer classification and identification of biomarkers, drug targets, and cancer-driving genes. The paper also examines applying machine learning algorithms like SVM to predict outbreaks of chronic diseases in populations using medical data.
3. The researchers aim to improve disease prediction accuracy by addressing issues with incomplete or inconsistent medical data from different regions. They also seek to enable early diagnosis and treatment by analyzing large healthcare datasets with machine learning.
Computer systems and the internet have greatly improved healthcare in several ways:
1. Electronic medical records allow doctors to access complete patient histories instantly and share information between hospitals. Computerized prescriptions reduce errors.
2. Diagnostic tools like CT scans, MRIs, and ultrasounds can identify medical issues much faster and more accurately than before. Monitoring equipment keeps close tabs on patients' vital signs.
3. Treatments are also enhanced through robotics in surgery, pacemakers, ventilators, and prosthetics that can mimic natural limb movement. Online support groups and research databases help patients.
4. However, self-diagnosis online risks missing issues, and purchasing medications without a prescription
cognitive computing for electronic medical record selamu shirtawi
This document discusses applying cognitive computing to electronic medical records (EMRs) using IBM Watson. It describes a cognitive computing system called Watson EMRA that can generate a problem-oriented summary of a patient's EMR. The summary aggregates key data like problems, medications, labs, notes, and procedures. It also identifies relationships between these data aggregates to present them in a clinically meaningful way. This type of cognitive system has the potential to reduce physicians' cognitive load when reviewing patient records and fulfilling their various information needs in clinical workflows.
The document discusses the field of health informatics and provides definitions and examples. It defines health informatics as the application of information science to healthcare and biomedical research. It describes the relationships between health informatics and other fields like computer science, engineering, and the medical sciences. The document also discusses different areas of health informatics like clinical informatics, public health informatics, and consumer health informatics. It provides examples of common health information technologies used in healthcare settings like electronic health records, computerized physician order entry, and picture archiving systems.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document describes a proposed method for designing a classifier to detect diabetes using neural networks and the fuzzy k-nearest neighbor algorithm. The method would train a neural network using the fuzzy k-NN algorithm on a server and use it to classify diabetes on a mobile device for convenience. Analysis in WEKA showed the method achieved around 72-74% accuracy on 10-fold cross validation of a diabetes dataset with attributes removed. The proposed method is expected to perform comparably to support vector machines with less complexity.
IRJET- A System for Complete Healthcare Management: Ask-Us-Health A Secon...IRJET Journal
This document proposes a system called ASK-US-HEALTH that uses machine learning algorithms and data mining to provide healthcare management. It aims to help patients access a second medical opinion by entering symptoms and receiving the probable diagnosis. It would also provide doctor recommendations and store patient medical histories and prescriptions. The system intends to improve healthcare access and help manage patient care and data for research through connecting patients, doctors, and nearby pharmacies via a web application.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESpijans
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
INTERNET OF THINGS BASED MODEL FOR IDENTIFYING PEDIATRIC EMERGENCY CASESamsjournal
Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and
categorize emergency cases for precise action. Current systems use manual examination resulting in
delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of
Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for
determining emergency cases, in pediatric section, specifically the triage section. It is later tested using
hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting
up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it
to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform
machine learning on the data by training a model and finally develop a Plotly Dash analytical application
integrating the model for visualization near real-time.Findings show that emergency cases are detected
using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The
model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT
gadgets and machine learning classification.
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
IRJET- Disease Analysis and Giving Remedies through an Android ApplicationIRJET Journal
The document describes a proposed Android application that uses decision trees to analyze symptoms and predict diseases. User-reported symptoms would be input to predict the disease and provide herbal remedies. The proposed system aims to overcome limitations of prior work by covering more diseases and their home remedies without side effects. It was developed using Android Studio and stores data in Firebase. The system uses a decision tree algorithm to predict disease based on symptom probability and scans a database to match remedies.
Analysis and Prediction of Diabetes Diseases using Machine Learning Algorithm...IRJET Journal
This document discusses several machine learning algorithms that have been used to predict diabetes, including KNN, Naive Bayes, Random Forest, J48, SVM, logistic regression, decision trees, neural networks, and ensemble models. It analyzes past research applying these methods to diabetes prediction and reports their accuracy results. The document then proposes using an ensemble hybrid model combining KNN, Naive Bayes, Random Forest, and J48 algorithms to predict diabetes with increased performance and accuracy compared to individual techniques.
2012 02 10 - Vreeman - Possibilities and Implications of ICF-powered Health I...dvreeman
The document discusses the possibilities and implications of using the International Classification of Functioning (ICF) to power health information technology. It describes how incorporating standardized vocabularies like ICF and LOINC into electronic health records could allow for data reuse across settings, clinical decision support, and a more seamless exchange of health information. This would help realize the vision of a healthcare system with coordinated, consumer-centered care enabled by digital tools.
IRJET- Survey on Risk Estimation of Chronic Disease using Machine LearningIRJET Journal
This document summarizes research on using machine learning to predict chronic disease risk. It discusses how healthcare generates massive amounts of data that can be used for prediction. The paper proposes a new convolutional neural network (CNN) based model that uses both structured and unstructured data from hospitals to predict disease risk. It compares this multimodal approach to existing unimodal prediction models. The document also reviews several other studies applying machine learning to tasks like heart disease prediction using large healthcare datasets. The goal is to develop effective machine learning models for predicting disease outbreaks in communities using real hospital data.
Multiple disease prediction using Machine Learning AlgorithmsIRJET Journal
This document discusses a proposed system for predicting multiple diseases using machine learning algorithms. It aims to predict diabetes, brain tumors, heart disease, and Alzheimer's disease using factors like age, sex, BMI, blood glucose levels, and other health parameters. Previous systems could only predict single diseases. The proposed system uses TensorFlow, Flask API, and machine learning techniques. It saves models using Python pickling and loads them using unpickling when needed. The system allows adding new disease prediction models. It analyzes full disease impacts by considering all contributing factors. This allows better prediction accuracy compared to existing single-disease models.
This document presents a health analyzer system that uses machine learning to predict multiple diseases from user-input data. The system was designed to predict diabetes, stroke, breast cancer, fetal health, liver disease, and heart disease. It uses various machine learning algorithms like random forest, SVM, logistic regression, naive bayes and decision trees. Models for each disease were trained on different datasets and the best performing algorithm was selected for each disease. A Flask API with user interfaces was created to allow users to input data and receive predictions. The system aims to provide a cost-effective solution compared to separate systems for each disease. It analyzes diseases by considering all relevant parameters to detect effects more accurately.
In this research work we have developed a strategy in which the various parameters that influence the occurrence of pulmonary disease have been gathered from survey of doctors who specialize in diagnoses of pulmonary disease and diagnostic recipes involving if the else rules were built and given labels, which were used as target for machine learning algorithms [Logistic , SVM, RBF, Naïve Bayes ] for identification of input dataset of symptoms of subjects . Multiple designs of these classifiers were implemented and best possible machine algorithm was identified for implementing the complete methodology. Results shows that there was no absolute answer for the design and selection of best possible machine algorithm as evident from the results based on multiple statistical tests, therefore , distance from ideal values of statistical test to find best classifier with most optimized parameters was calculated and the classifier which had closest to these ideal values was found and declared the best classifier for identification of pulmonary diseases presence or absence .as per results naïve bayes classifier is performing best which is evident from the statistical test scores .
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Multi Disease Detection using Deep LearningIRJET Journal
1) The document proposes a system for multi-disease detection using deep learning that could provide early detection of chronic diseases like heart disease, cancer, and diabetes from medical data and save lives.
2) It reviews literature on disease prediction using machine learning algorithms like CNN, KNN, decision trees, and support vector machines. CNN showed slightly better accuracy than KNN for general disease detection.
3) The proposed system would use deep learning models to detect and classify diseases from medical images and data with high accuracy, helping doctors verify test results and enhancing their experience with diseases. It aims to reduce the costs of diagnostic testing for chronic conditions.
1) The team aims to address the lack of accessible healthcare in India by building machine learning tools for rapid screening and diagnosis using large healthcare datasets.
2) They conducted a field study collecting health data from 500 patients using various devices and built annotation tools for doctors to label the data.
3) The goal is to develop automated screening, detection, and diagnosis machine learning models trained on this annotated data to assist doctors and increase accessibility of healthcare.
IRJET - Prediction and Analysis of Multiple Diseases using Machine Learni...IRJET Journal
This document discusses using machine learning techniques to predict and analyze multiple diseases. It presents research using KNN, support vector machine, random forest, and decision tree algorithms applied to a medical database to predict future and previous diseases. The goal is to provide a smart card method for easily and accurately diagnosing disease by storing an individual's full medical record. It reviews related work applying various machine learning classifiers like decision trees, naive Bayes, and logistic regression to diseases such as heart disease, diabetes, and cancer. The conclusion is that machine learning applied to medical data can help predict disease and save time for patients and doctors.
1. The document describes a multiple disease prediction system that uses machine learning to predict three diseases: heart disease, liver disease, and diabetes.
2. It aims to build a single system that can predict multiple diseases, unlike existing systems that typically only predict one disease. This would allow users to predict different diseases without needing multiple different tools.
3. The system is designed to take user inputs related to symptoms and features of the selected disease and use machine learning algorithms like KNN, random forest and XGBoost trained on disease datasets to predict the likelihood of the disease. The models would be integrated into a web interface using Django for users to get predictions.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
This document discusses applying machine learning algorithms to predict chronic kidney disease. It:
1) Applied three algorithms (C4.5 decision tree, SVM, and Bayesian Network) to a chronic kidney disease dataset containing 400 patients and 24 attributes to classify patients as having chronic kidney disease or not.
2) Found that the C4.5 decision tree algorithm had the best performance based on accuracy (63%), error rate (0.37), kappa statistic (0.97), and other evaluation metrics. SVM and Bayesian Network performance was lower.
3) Concludes C4.5 decision tree is the most efficient algorithm for predicting chronic kidney disease based on this medical dataset.
AN IMPROVED MODEL FOR CLINICAL DECISION SUPPORT SYSTEMijaia
The document describes an improved model for a clinical decision support system that was developed to address issues with misdiagnosis and inconsistent healthcare records. The system incorporates both knowledge-based and non-knowledge based decision support methods using a hybrid approach. It was trained and validated using prostate cancer and diabetes datasets, achieving classification accuracies of 98% and 94% respectively. The system aims to enhance disease detection and prediction to support better healthcare delivery.
IRJET - Detecting Pneumonia from Chest X-Ray Images using Committee MachineIRJET Journal
This document discusses detecting pneumonia from chest X-ray images using a committee machine. It aims to build a web application that can accurately diagnose pneumonia by analyzing chest X-ray images. The system will use a dataset of over 5,800 chest X-ray images to predict if an image shows pneumonia. It will apply techniques like image processing, machine learning algorithms, and a committee machine to increase the accuracy of pneumonia detection compared to other methods.
Plug In Generator To Produce Variant Outputs For Unique Data.IJRES Journal
Our modern world comprising of abundant chronic diseases which are affecting humankind, awful thing is that they affect the people without being notified until the end. In this project we proposed a system in which the user identifies the disease by providing the symptoms which he is experiencing. The user selects the multiple symptoms which he/she is suffering and submits them for evaluation using String Matching System. The database consists of limited number of diseases, with well organized pattern structure of symptoms. Using a friendly interface, user can input the data in the questionnaire form developed. Artificial Bee Colony Optimization [ABC] algorithm, i.e., a Machine Learning algorithm embedded in the project provides an optimistic disease along with its prevention and curing methods, but before ABC produces optimistic disease, String Matching System approach gives an accurate disease with which the human is suffering from. The above said data transformed into web can be considered as an offline browsing system which can be used by any educated personalities, to generally know what is happening and gets enough idea before visiting the practitioner.
This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environmentnooriasukmaningtyas
The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
This document describes an advanced machine learning approach for predicting skin cancer. It discusses using machine learning algorithms like Naive Bayes, Decision Tree, Random Forest on a dataset to estimate disease risk and determine algorithm accuracy. The paper focuses on developing a system that integrates symptom and medical data using machine learning algorithms like K-means to provide accurate disease predictions.
IRJET - Digital Assistance: A New Impulse on Stroke Patient Health Care using...IRJET Journal
1) The document presents a study that uses deep learning algorithms and artificial bee colony optimization to predict stroke using medical dataset features.
2) A neural network architecture is developed to classify patients' risk of stroke based on 13 variables from their medical records, with the artificial bee colony algorithm used to preprocess data and extract meaningful features.
3) The random forest algorithm achieved the highest prediction accuracy of over 88% based on metrics like precision, recall, and F1 score compared to other models like logistic regression, naive bayes, and decision trees.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Similar to Proposed Model for Chest Disease Prediction using Data Analytics (20)
Understanding the Impact and Challenges of Corona Crisis on Education Sector...vivatechijri
n the second week of March 2020, governments of all states in a country suddenly declared
shutting down of all colleges and schools for a temporary period of time as an immediate measure to stop the
spread of pandemic that is of novel corona virus. As the days pass by almost close to a month with no certainty
when they will again reopen. Due to pandemic like this an alarm bells have started sounding in the field of
education where a huge impact can be seen on teaching and learning process as well as on the entire education
sector in turn. The pandemic disruption like this is actually gave time to educators of today to really think about
the sector. Through the present research article, the author is highlighting on the possible impact of
coronavirus on education sector with the future challenges for education sector with possible suggestions.
LEADERSHIP ONLY CAN LEAD THE ORGANIZATION TOWARDS IMPROVEMENT AND DEVELOPMENT vivatechijri
This document discusses the importance of leadership in leading an organization towards improvement and development. It states that leadership is responsible for providing a clear vision and strategy to successfully achieve that vision. Effective leadership can impact the success of an organization by controlling its direction and motivating employees. Leadership is different from traditional management in that it guides employees towards organizational goals through open communication and motivation, rather than simply directing work. The paper concludes that only leadership can lead an organization to change according to its evolving environment, while management may simply follow old rules. Leadership is key to adapting to new market needs and trends.
The topic of assignment is a critical problem in mathematics and is further explored in the real
physical world. We try to implement a replacement method during this paper to solve assignment problems with
algorithm and solution steps. By using new method and computing by existing two methods, we analyse a
numerical example, also we compare the optimal solutions between this new method and two current methods. A
standardized technique, simple to use to solve assignment problems, may be the proposed method
Structural and Morphological Studies of Nano Composite Polymer Gel Electroly...vivatechijri
The document summarizes research on a nano composite polymer gel electrolyte containing SiO2 nanoparticles. Key points:
1. Polyvinylidene fluoride-co-hexafluoropropylene polymer was used as the base polymer mixed with propylene carbonate, magnesium perchlorate, and SiO2 nanoparticles to synthesize the nano composite polymer gel electrolyte.
2. The electrolyte was characterized using XRD, SEM, and FTIR which confirmed the homogeneous dispersion of SiO2 nanoparticles and increased amorphous nature of the electrolyte, enhancing its ion conductivity.
3. XRD showed decreased crystallinity and disappearance of polymer peaks upon addition of SiO2. SEM revealed
Theoretical study of two dimensional Nano sheet for gas sensing applicationvivatechijri
This study is focus on various two dimensional material for sensing various gases with theoretical
view for new research in gas sensing application. In this paper we review various two dimensional sheet such as
Graphene, Boron Nitride nanosheet, Mxene and their application in sensing various gases present in the
atmosphere.
METHODS FOR DETECTION OF COMMON ADULTERANTS IN FOODvivatechijri
Food is essential forliving. Food adulteration deceives consumers and can endanger their health. The
purpose of this document is to list common food adulterant methods commonly found in India. An adulterant is
a substance found in other substances such as food, cosmetics, pharmaceuticals, fuels, or other chemicals that
compromise the safety or effectiveness of that substance. The addition of adulterants is called adulteration. The
most common reason for adulteration is the use of undeclared materials by manufacturers that are cheaper than
the correct and declared ones. The adulterants can be harmful or reduce the effectiveness of the product, or
they can be harmless.
The novel ideas of being a entrepreneur is a key for everyone to get in the hustle, but developing a
idea from core requires a systematic plan, time management, time investment and most importantly client
attention. The Time required for developing may vary from idea to idea and strength of the team. Leadership to
build a team and manage the same throughout the peak of development is the main quality. Innovations and
Techniques to qualify the huddles is another aspect of Business Development and client Retention.
Innovation for supporting prosperity has for quite some time been a focus on numerous orders, including PC science, brain research, and human-PC connection. In any case, the meaning of prosperity isn't continuously clear and this has suggestions for how we plan for and evaluate advances that intend to cultivate it. Here, we talk about current meanings of prosperity and how it relates with and now and then is a result of self-amazing quality. We at that point center around how innovations can uphold prosperity through encounters of self-amazing quality, finishing with conceivable future bearings.
An Alternative to Hard Drives in the Coming Future:DNA-BASED DATA STORAGEvivatechijri
Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up, there emerges a requirement for a storage medium with high capacity, high storage density, and possibility to face up to extreme environmental conditions. According to a research in 2018, every minute Google conducted 3.88 million searches, other people posted 49,000 photos on Instagram, sent 159,362,760 e-mails, tweeted 473,000 times and watched 4.33 million videos on YouTube. In 2020 it estimated a creation of 1.7 megabytes of knowledge per second per person globally, which translates to about 418 zettabytes during a single year. The magnetic or optical data-storage systems that currently hold this volume of 0s and 1s typically cannot last for quite a century. Running data centres takes vast amounts of energy. In short, we are close to have a substantial data-storage problem which will only become more severe over time. Deoxyribonucleic acid (DNA) are often potentially used for these purposes because it isn't much different from the traditional method utilized in a computer. DNA’s information density is notable, 215 petabytes or 215 million gigabytes of data can be stored in just one gram of DNA. First we can encode all data at a molecular level and then store it in a medium that will last for a while and not become out-dated just like floppy disks. Due to the improved techniques for reading and writing DNA, a rapid increase is observed in the amount of possible data storage in DNA.
The usage of chatbots has increased tremendously since past few years. A conversational interface is an interface that the user can interact with by means of a conversation. The conversation can occur by speech but also by text input. When a chatty interface uses text, it is also described as a chatbot or a conversational medium. During this study, the user experience factors of these so called chatbots were investigated. The prime objective is “to spot the state of the art in chatbot usability and applied human-computer interaction methodologies, to research the way to assess chatbots usability". Two sorts of chatbots are formulated, one with and one without personalisation factors. the planning of this research may be a two-by-two factorial design. The independent variables are the two chatbots (unpersonalised versus personalised) and thus the speci?c task or goal the user are ready to do with the chatbot within the ?nancial ?eld (a simple versus a posh task). The results are that there was no noteworthy interaction effect between personalisation and task on the user experience of chatbots. A signi?cant di?erence was found between the two tasks with regard to the user experience of chatbots, however this variation wasn't because of personalisation.
The Smart glasses Technology of wearable computing aims to identify the computing devices into today’s world.(SGT) are wearable Computer glasses that is used to add the information alongside or what the wearer sees. They are also able to change their optical properties at runtime.(SGT) is used to be one of the modern computing devices that amalgamate the humans and machines with the help of information and communication technology. Smart glasses is mainly made up of an optical head-mounted display or embedded wireless glasses with transparent heads- up display or augmented reality (AR) overlay in it. In recent years, it is been used in the medical and gaming applications, and also in the education sector. This report basically focuses on smart glasses, one of the categories of wearable computing which is very popular presently in the media and expected to be a big market in the next coming years. It Evaluate the differences from smart glasses to other smart devices. It introduces many possible different applications from the different companies for the different types of audience and gives an overview of the different smart glasses which are available presently and will be available after the next few years.
Future Applications of Smart Iot Devicesvivatechijri
With the Internet of Things (IoT) bit by bit creating as the resulting time of the headway of the Internet, it gets critical to see the diverse expected zones for the utilization of IoT and the research challenges that are connected with these applications going from splendid savvy urban areas, to medical care administrations, shrewd farming, collaborations and retail. IoT is needed to attack into for all expectations and purposes for all pieces of our day-to-day life. Despite the fact that the current IoT enabling advancements have immensely improved in the continuous years, there are so far different issues that require attention. Since the IoT ideas results from heterogeneous advancements, many examination difficulties will arise. In like manner, IoT is planning for new components of exploration to be finished. This paper presents the progressing headway of IoT advancements and inspects future applications.
Cross Platform Development Using Fluttervivatechijri
Today the development of cross-platform mobile application has under the state of compromise. The developers are not willing to choose an alternative of either building the similar app many times for many operating systems or to accept a lowest common denominator and optimal solution that will going to trade the native speed, accuracy for portability. The Flutter is an open-source SDK for creating high-performance, high fidelity mobile apps for the development of iOS and Android. Few significant features of flutter are - Just-in-time compilation (JIT), Ahead- of-time compilation (AOT compilation) into a native (system-dependent) machine code so that the resulting binary file can execute natively. The Flutter’s hot reload functionality helps us to understand quickly and easily experiment, build UIs, add features, and fix bugs. Hot reload works by injecting updated source code files into the running Dart Virtual Machine (VM). With the help of Flutter, we believe that we would be having a solution that gives us the best of both worlds: hardware accelerated graphics and UI, powered by native ARM code, targeting both popular mobile operating systems.
The Internet, today, has become an important part of our lives. The World Wide Web that was once a small and inaccessible data storage service is now large and valuable. Current activities partially or completely integrated into the physical world can be made to a higher standard. All activities related to our daily life are mapped and linked to another business in the digital world. The world has seen great strides in the Internet and in 3D stereoscopic displays. The time has come to unite the two to bring a new level of experience to the users. 3D Internet is a concept that is yet to be used and requires browsers to be equipped with in-depth visualization and artificial intelligence. When this material is included, the Internet concept of material may become a reality discussed in this paper. In this paper we have discussed the features, possible setting methods, applications, and advantages and disadvantages of using the Internet. With this paper we aim to provide a clear view of 3D Internet and the potential benefits associated with this obviously cost the amount of investment needed to be used.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
The study LiFi (Light Fidelity) demonstrates about how can we use this technology as a medium of communication similar to Wifi . This is the latest technology proposed by Harold Haas in 2011. It explains about the process of transmitting data with the help of illumination of an Led bulb and about its speed intensity to transmit data. Basically in this paper, author will discuss about the technology and also explain that how we can replace from WiFi to LiFi . WiFi generally used for wireless coverage within the buildings while LiFi is capable for high intensity wireless data coverage in limited areas with no obstacles .This research paper represents introduction of the Lifi technology,performance,modulation and challenges. This research paper can be used as a reference and knowledge to develop some of LiFitechnology.
Social media platform and Our right to privacyvivatechijri
The advancement of Information Technology has hastened the ability to disseminate information across the globe. In particular, the recent trends in ‘Social Networking’ have led to a spark in personally sensitive information being published on the World Wide Web. While such socially active websites are creative tools for expressing one’s personality it also entails serious privacy concerns. Thus, Social Networking websites could be termed a double edged sword. It is important for the law to keep abreast of these developments in technology. The purpose of this paper is to demonstrate the limits of extending existing laws to battle privacy intrusions in the Internet especially in the context of social networking. It is suggested that privacy specific legislation is the most appropriate means of protecting online privacy. In doing so it is important to maintain a balance between the competing right of expression, the failure of which may hinder the reaping of benefits offered by Internet technology
THE USABILITY METRICS FOR USER EXPERIENCEvivatechijri
THE USABILITY METRICS FOR USER EXPERIENCE was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as THE USABILITY METRICS FOR USER EXPERIENCE that is GFS. THE USABILITY METRICS FOR USER EXPERIENCE is one of the largest file system in operation. Generally THE USABILITY METRICS FOR USER EXPERIENCE is a scalable distributed file system of large distributed data intensive apps. In the design phase of THE USABILITY METRICS FOR USER EXPERIENCE, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. THE USABILITY METRICS FOR USER EXPERIENCE also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, THE USABILITY METRICS FOR USER EXPERIENCE is highly available, replicas of chunk servers and master exists.
Google File System was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as Google File System that is GFS. Google File system is one of the largest file system in operation. Generally Google File System is a scalable distributed file system of large distributed data intensive apps. In the design phase of Google file system, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. Google File System also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, Google file system is highly available, replicas of chunk servers and master exists.
A Study of Tokenization of Real Estate Using Blockchain Technologyvivatechijri
Real estate is by far one of the most trusted investments that people have preferred, being a lucrative investment it provides a steady source of income in the form of lease and rents. Although there are numerous advantages, one of the key downsides of real estate investments is lack of liquidity. Thus, even though global real estate investments amount to about twice the size of investments in stock markets, the number of investors in the real estate market is significantly lower. Block chain technology has real potential in addressing the issues of liquidity and transparency, opening the market to even retail investors. Owing to the functionality and flexibility of creating Security Tokens, which are backed by real-world assets, real estate can be made liquid with the help of Special Purpose Vehicles. Tokens of ERC 777 standard, which represent fractional ownership of the real estate can be purchased by an investor and these tokens can also be listed on secondary exchanges. The robustness of Smart Contracts can enable the efficient transfer of tokens and seamless distribution of earnings amongst the investors. This work describes Ethereum blockchainbased solutions to make the existing Real Estate investment system much more efficient.
Software Engineering and Project Management - Introduction to Project ManagementPrakhyath Rai
Introduction to Project Management: Introduction, Project and Importance of Project Management, Contract Management, Activities Covered by Software Project Management, Plans, Methods and Methodologies, some ways of categorizing Software Projects, Stakeholders, Setting Objectives, Business Case, Project Success and Failure, Management and Management Control, Project Management life cycle, Traditional versus Modern Project Management Practices.
Encontro anual da comunidade Splunk, onde discutimos todas as novidades apresentadas na conferência anual da Spunk, a .conf24 realizada em junho deste ano em Las Vegas.
Neste vídeo, trago os pontos chave do encontro, como:
- AI Assistant para uso junto com a SPL
- SPL2 para uso em Data Pipelines
- Ingest Processor
- Enterprise Security 8.0 (Maior atualização deste seu release)
- Federated Analytics
- Integração com Cisco XDR e Cisto Talos
- E muito mais.
Deixo ainda, alguns links com relatórios e conteúdo interessantes que podem ajudar no esclarecimento dos produtos e funções.
https://www.splunk.com/en_us/campaigns/the-hidden-costs-of-downtime.html
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-leading-observability-practice.pdf
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-modern-security-program.pdf
Nosso grupo oficial da Splunk:
https://usergroups.splunk.com/sao-paulo-splunk-user-group/
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!
OCS Training Institute is pleased to co-operate with
a Global provider of Rig Inspection/Audits,
Commission-ing, Compliance & Acceptance as well as
& Engineering for Offshore Drilling Rigs, to deliver
Drilling Rig Inspec-tion Workshops (RIW) which
teaches the inspection & maintenance procedures
required to ensure equipment integrity. Candidates
learn to implement the relevant standards &
understand industry requirements so that they can
verify the condition of a rig’s equipment & improve
safety, thus reducing the number of accidents and
protecting the asset.
20CDE09- INFORMATION DESIGN
UNIT I INCEPTION OF INFORMATION DESIGN
Introduction and Definition
History of Information Design
Need of Information Design
Types of Information Design
Identifying audience
Defining the audience and their needs
Inclusivity and Visual impairment
Case study.
Best Practices of Clothing Businesses in Talavera, Nueva Ecija, A Foundation ...IJAEMSJORNAL
This study primarily aimed to determine the best practices of clothing businesses to use it as a foundation of strategic business advancements. Moreover, the frequency with which the business's best practices are tracked, which best practices are the most targeted of the apparel firms to be retained, and how does best practices can be used as strategic business advancement. The respondents of the study is the owners of clothing businesses in Talavera, Nueva Ecija. Data were collected and analyzed using a quantitative approach and utilizing a descriptive research design. Unveiling best practices of clothing businesses as a foundation for strategic business advancement through statistical analysis: frequency and percentage, and weighted means analyzing the data in terms of identifying the most to the least important performance indicators of the businesses among all of the variables. Based on the survey conducted on clothing businesses in Talavera, Nueva Ecija, several best practices emerge across different areas of business operations. These practices are categorized into three main sections, section one being the Business Profile and Legal Requirements, followed by the tracking of indicators in terms of Product, Place, Promotion, and Price, and Key Performance Indicators (KPIs) covering finance, marketing, production, technical, and distribution aspects. The research study delved into identifying the core best practices of clothing businesses, serving as a strategic guide for their advancement. Through meticulous analysis, several key findings emerged. Firstly, prioritizing product factors, such as maintaining optimal stock levels and maximizing customer satisfaction, was deemed essential for driving sales and fostering loyalty. Additionally, selecting the right store location was crucial for visibility and accessibility, directly impacting footfall and sales. Vigilance towards competitors and demographic shifts was highlighted as essential for maintaining relevance. Understanding the relationship between marketing spend and customer acquisition proved pivotal for optimizing budgets and achieving a higher ROI. Strategic analysis of profit margins across clothing items emerged as crucial for maximizing profitability and revenue. Creating a positive customer experience, investing in employee training, and implementing effective inventory management practices were also identified as critical success factors. In essence, these findings underscored the holistic approach needed for sustainable growth in the clothing business, emphasizing the importance of product management, marketing strategies, customer experience, and operational efficiency.
Response & Safe AI at Summer School of AI at IIITHIIIT Hyderabad
Talk covering Guardrails , Jailbreak, What is an alignment problem? RLHF, EU AI Act, Machine & Graph unlearning, Bias, Inconsistency, Probing, Interpretability, Bias
A vernier caliper is a precision instrument used to measure dimensions with high accuracy. It can measure internal and external dimensions, as well as depths.
Here is a detailed description of its parts and how to use it.
Unblocking The Main Thread - Solving ANRs and Frozen FramesSinan KOZAK
In the realm of Android development, the main thread is our stage, but too often, it becomes a battleground where performance issues arise, leading to ANRS, frozen frames, and sluggish Uls. As we strive for excellence in user experience, understanding and optimizing the main thread becomes essential to prevent these common perforrmance bottlenecks. We have strategies and best practices for keeping the main thread uncluttered. We'll examine the root causes of performance issues and techniques for monitoring and improving main thread health as wel as app performance. In this talk, participants will walk away with practical knowledge on enhancing app performance by mastering the main thread. We'll share proven approaches to eliminate real-life ANRS and frozen frames to build apps that deliver butter smooth experience.
Online music portal management system project report.pdfKamal Acharya
The iMMS is a unique application that is synchronizing both user
experience and copyrights while providing services like online music
management, legal downloads, artists’ management. There are several
other applications available in the market that either provides some
specific services or large scale integrated solutions. Our product differs
from the rest in a way that we give more power to the users remaining
within the copyrights circle.
Online music portal management system project report.pdf
Proposed Model for Chest Disease Prediction using Data Analytics
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Proposed Model for Chest Disease Prediction using Data
Analytics
Vikrant A. Agaskar 1
and Umesh Kulkarni2
1
(PG Student ARMIET, Dist. Thane, India)
2
(Vidyalankar Institute of Technology, India)
Abstract: Chest diseases if not properly diagnosed in early stages can be fatal. Because of lack of skilled
knowledge or experiences of real life practitioners, many a times one chest disease is wrongly diagnosed for the
other, which leads to wrong treatment. Due to this the actual disease keeps on growing and become fatal. For
example, muscular chest pains can be treated for the heart disease or COPD is treated for Asthma. Early
prediction of chest disease is crucial but is not an easy task. Consequently, the computer based prediction system
for chest disease may play a significant role as a pre-stage detection to take proper actions with a view to recover
from it. However the choice of the proper Data Mining classification method can effectively predict the early
stage of the disease for being cured from it. In this paper, the three mostly used classification techniques such as
support vector machine (SVM), k-nearest neighbour (KNN) and artificial neural network (ANN) have been studied
with a view to evaluating them for chest disease prediction.
Keywords – KNN, SVM, Data Analysis, ANN.
1. INTRODUCTION
Human beings suffer from a wide variety of chest-related diseases. These chest diseases include asthma,
copd, pneumonia, tuberculosis, etc. The chest diseases have symptoms that demonstrate their presence. Symptoms
include shortness of breath, chest congestion, chest pain, cough from the throat, and cough from the chest, etc.
and manifest in difference, these are the common symptoms which are found in many situations. When human
beings do regular functions in their day to day lives, they are prone to seek these symptoms in situations such as
running, walking, long breathing up, etc. To detect which chest disease the human being might be facing, a plan
is identified by which decision can be made by making use of a symptom-based questionnaire. To make the
machine understand and predict which disease the patient suffers from, it must be trained on the sample datasets
containing symptoms in questionnaire. Such datasets can be obtained from UCI database, CHHS (California
health and human services) database, as well as data from reputed national institute of tuberculosis and respiratory
diseases.
A large number of people who suffer from chest related diseases die due to wrong predication of chest
conditions. This is often due to the fact that they are diagnosed much later after the disease occurs, after which it
becomes difficult to solve the problem. In addition to this, they are often misdiagnosed for one another. A patient
with Asthma may be told he has COPD and vice versa since there is a very thin line difference between these two
diseases. Initially they are so identical that hardly difference is there. This leads to the wrong treatment being
given to the patient and causes adverse effects of the treatment. Therefore, there is a need to build an easy system
to aid doctors for preliminary decision making. There is also a need to empower the patient with a tool that helps
him understand his condition better and take appropriate measures by giving proper information of his condition
of health to the correct doctor.
Mainly focus is on collection of information for Knowledge Discovery in Databases (KDD). This is
initial process from which mashup candidates are identified by addressing a repository of open services. Within
this approach, there is a customized approach to life cycle which software engineers can use to generate new
applications based on service integration techniques. KDD also define service integration qualification by
discovering different aspects of web service specifications.
2. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 2 (2019)
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2. AVAILABLE METHODS
a. Support Vector Machine (SVM)
Support vector machine is a supervised learning model that is defined as the finite dimensional vector
spaces where each dimension characterizes a feature of a particular object. In this way, SVM has been proved as
an effective method in high-dimensional space problems. Due to its computational competence on huge datasets
SVM is typically used in document classification, sentiment analysis and prediction-based tasks
b. K-Nearest Neighbors (KNN)
K-Nearest Neighbor (KNN), a supervised learning model as well, is used to classify the test data using
the training samples directly. In KNN, an object is classified by the majority voting of its closest neighbors.
Alternatively, the class of a new sample is predicted based on some distance metrics where the distance metric
can be a simple Euclidean distance. In the working steps, KNN first calculates k (No. of the nearest neighbors).
After that, it finds the distance between the training data and then sorts the distance. Subsequently, a class label
will be assigned to the test data based on the majority voting.
c. Artificial Neural Network (ANN)
The Artificial Neural Network (ANN), also a supervised learning strategy, contains three layers: input,
hidden and output. The connection between the input units and the hidden and the output units are based on
relevance of the assigned weight of that specific input unit. Usually, if the weight is higher, then it is considered
more important. ANN may use linear and sigmoid transfer (activation) functions. Also, the ANNs are suitable for
the training of large amounts of data with limited inputs. For multi-layer feed forward ANN, the mostly used
learning algorithm is the Backpropagation learning tool. In ANN, the input data records should be separated into
three sub-datasets for the purpose of training, validation and testing.
3. PROPOSED METHOD
Symptom-based Questionnaires required: Asthma, COPD, Pneumonia, Tuberculosis.
Training of machine using datasets:
a. Dataset required for the purpose can be obtained from UCI repository database, CSSH database
b. Datasets from the National Institute of Tuberculosis and Respiratory Diseases (India).
c. An ML training service like Tensor Flow can be used to train the system based on the dataset
selected.
d. A Cloud ML service can be used to verify and double check the training.
e. Predictive analysis is carried out to find the % of accuracy diagnosis for a particular disease
The main steps which are considered when a predicated disease is to be notified.
Step 1. Collection of user data
Step 2. User choice
Step 3. Collection of questionnaire
Step 4. Processing of data collection
Step 5. Comparing of the data received with data set
Step 6. Result of comparison decision to be taken with respected to which chest disease
Step 7. Depending on the decision proceed for treatment, if ok update the data set
Currently systems utilize a large amount of medical data taken from tests that determine the nature of the
chest disease. These are expensive and not scalable in nature and require advanced medical professionals. To
overcome problems on existing system, in proposed system user does not require to search data in various
repository with special features. User need only to give information which is required to be collected. User can
just type combination of queries and based on user behaviour analysis exact data will be predicted.
However, over the years medical researchers have arrived at a synthesis of this medical data to give us
symptom-based questionnaires that can be used by people to detect these diseases. But the limitations of these
questionnaires are that they have been arrived at in small clinical trials using small amounts of patient and control
data.
Therefore, there is a need to build a machine learning system that uses large amounts of patient and
control data to verify and use these symptoms based questionnaires for the broader public. We seek to integrate
several of these symptom-based questionnaires with real life scenario data to e able to precisely and yet easily
predict which chest disease the patient has. There are two kinds of data required, patient data (chest disease
patients and their symptoms) and control data of normal people with no chest conditions. By integrating these
data sets, to create weighted scores for each question in the questionnaire, we will be able to generate a result of
which chest disease the patient is suffering.
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Fig 1. Flow chart.
The main focus of Predictive Diagnosis System will be to implement machine learning algorithms and
Prediction of which chest disease the patient might be suffering from based on the symptoms.
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4. ANALYSIS AND ADVANTAGES
There are important applications for this type of systems there are:
a. In hospitals for doctors to use as an initial diagnosis measure before further check-ups.
b. For self-diagnosis by patients
c. By government and municipal bodies to see the impact of air pollution on health of citizens.
d. Industries to ensure health and welfare of people before setting up manufacturing and other plants
near occupied localities.
Major advantage of the proposed system is that it is generally very difficult to predict chest diseases other
than heart disease as data and specific criteria’s to diagnose chest diseases are not available. All the system which
are available currently are focusing only on the heart disease prediction. Periodic record of PFT (Pulmonary
function test) gives regular input about the patient’s condition. This system can predict COPD and Asthma well
in their initial stages. COPD and Asthma can be controlled if diagnosed in initial stage itself. Whereas Pneumonia
and heart disease can even be diagnosed and treated as substantial research has already been done.
5. CONCLUSION
A prototype chest disease prediction system is developed using three data mining classification Modeling
techniques. The system extracts hidden knowledge from a historical chest disease database. DMX query language
and functions are used to build and access the models. The models are trained and validated against a test dataset.
Lift Chart and Classification Matrix methods are used to evaluate the effectiveness of the models. All three models
are able to extract patterns in response to the predictable state. The most effective model to predict patients with
chest disease appears to be Artificial Neural Network and Decision Trees. The goals are evaluated against the
trained models. All three models could answer complex queries, each with its own strength with respect to ease
of model interpretation, access to detailed information and accuracy. This system can be further enhanced and
expanded. It can also incorporate other data mining techniques, e.g., Time Series, and Association Rules.
Continuous data can also be used instead of just categorical data. Another area is to use Text Mining to mine the
vast amount of unstructured data available in healthcare databases.
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der Molen T, van Schayck “Symptom-Based Questionnaire for Identifying COPD in Smokers, Respiration”
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[7] Ken Farion Departments of Pediatrics and Emergency Medicine, University of Ottawa Ottawa, Canada Wojtek Michalowski, Szymon
Wilk1 , Dympna O’Sullivan Telfer School of Management, University of Ottawa Ottawa, Canada Stan Matwin School of Information
Technology and Engineering, University of Ottawa Ottawa, Canada Institute of Computer Science, Polish Academy of Sciences Warsaw,
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