The document summarizes a project that aims to build a multiple disease prediction system using machine learning. The system will use real-time health parameters as input to predict diabetes, heart disease, and Parkinson's disease. Literature on similar existing systems is reviewed. The objectives, design methodology, project plan, requirements and deliverables are outlined. The system will collect data, build models using algorithms like SVM and logistic regression, and deploy a web app interface. The project aims to provide timely disease prediction using a single platform to help improve health outcomes.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
Thank You for referencing this work, if you find it useful!
Vlad Manea, Katarzyna Wac, mQoL: Mobile Quality of Life Lab:
From Behavior Change to QoL, Mobile Human Contributions: Opportunities and Challenges (MHC) Workshop in conjunction with UBICOMP, Singapore, October 2018.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
With the promises of predictive analytics in big data, and the use of machine learning algorithms,
predicting future is no longer a difficult task, especially for health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers
to apply all the technical innovations like big data analytics, predictive analytics, machine learning and
learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
A Neural Network Based Diabetes Prediction on Imbalance Dataset.pptxshivani28yadav
Presented this research paper presentation in 10th International Conference on Communication Systems and Network Technologies(CSNT)-2021 .This research work was published by IEEE .The link mentioned below:
https://ieeexplore.ieee.org/document/9509732
As an extensively well-known chronic disease, diabetes is an illness that harms the body’s capability to process blood glucose. The proper treatment of diabetes could help a person live a long and normal life in general. It is necessary to detect the disease at an early stage. We focus our work on the performance of a machine-learning (ML) algorithm to identify the presence of diabetes on the PIMA Indian diabetes dataset (PIDD) which referenced from the University of California, Irvine (UCI) ML repository. Using ML, we know about the classification and prediction techniques. Further, diabetes became an attention seeker in the field of research due to the presence of imbalanced and missing data. Although many factors affect the performance of the algorithm, This research paper worked on the prediction technique for diabetes classification with outliers and missing values in data with class imbalance. Using an adaptive synthetic sampling method (ADASYN) and reduced the impact of class imbalance on the performance of the prediction model. Then, this algorithm improved the generalization using a feature selection technique and multilayer perceptron classifiers to make predictions and evaluations. Experimental results shows that this experiment obtained a better accuracy of 84% with a neural network model in comparison with the previous model.
Keywords are Diabetes prediction . Machine Learning. Outliers . Artificial Neural Network . Adaptive synthetic sampling . Multilayer Perceptron
report is a report that compiles all your roles, skills, experience, and exposure you had during an internship and your ongoing considerations. After completing an internship, an internship report is asked to understand the interns better for improvement in the future.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Thank You for referencing this work, if you find it useful!
Vlad Manea, Katarzyna Wac, mQoL: Mobile Quality of Life Lab:
From Behavior Change to QoL, Mobile Human Contributions: Opportunities and Challenges (MHC) Workshop in conjunction with UBICOMP, Singapore, October 2018.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
With the promises of predictive analytics in big data, and the use of machine learning algorithms,
predicting future is no longer a difficult task, especially for health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers
to apply all the technical innovations like big data analytics, predictive analytics, machine learning and
learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
A Neural Network Based Diabetes Prediction on Imbalance Dataset.pptxshivani28yadav
Presented this research paper presentation in 10th International Conference on Communication Systems and Network Technologies(CSNT)-2021 .This research work was published by IEEE .The link mentioned below:
https://ieeexplore.ieee.org/document/9509732
As an extensively well-known chronic disease, diabetes is an illness that harms the body’s capability to process blood glucose. The proper treatment of diabetes could help a person live a long and normal life in general. It is necessary to detect the disease at an early stage. We focus our work on the performance of a machine-learning (ML) algorithm to identify the presence of diabetes on the PIMA Indian diabetes dataset (PIDD) which referenced from the University of California, Irvine (UCI) ML repository. Using ML, we know about the classification and prediction techniques. Further, diabetes became an attention seeker in the field of research due to the presence of imbalanced and missing data. Although many factors affect the performance of the algorithm, This research paper worked on the prediction technique for diabetes classification with outliers and missing values in data with class imbalance. Using an adaptive synthetic sampling method (ADASYN) and reduced the impact of class imbalance on the performance of the prediction model. Then, this algorithm improved the generalization using a feature selection technique and multilayer perceptron classifiers to make predictions and evaluations. Experimental results shows that this experiment obtained a better accuracy of 84% with a neural network model in comparison with the previous model.
Keywords are Diabetes prediction . Machine Learning. Outliers . Artificial Neural Network . Adaptive synthetic sampling . Multilayer Perceptron
report is a report that compiles all your roles, skills, experience, and exposure you had during an internship and your ongoing considerations. After completing an internship, an internship report is asked to understand the interns better for improvement in the future.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
1. Project Review-0
on
MULTIPLE DISEASE PREDICTION SYSTEM
by
Lohiith Dinesh Ramiya (RA1911043010051)
G .Lakshmi Reethika(RA1911043010053)
A.S.Raghavendra (RA1911043010065)
Under the guidance of
Dr.S.Murugaveni
Assistant Professor, Departmentof ECE
3. Abstract
• Many of the existing machine learning models for health care analysis are concentrating
on one disease per analysis. Like one analysis if for diabetes analysis, one for cancer
analysis, one for skin diseases like that.
• There is no common system where one analysis can perform more than one disease
prediction. In this project proposing a system which used to predict multiple diseases by
using StreamLit API.
• In this project used to analyse Diabetes analysis, Heart disease and Parkinson’s analysis.
Later other diseases like skin diseases, fever analysis and many more diseases can be
included.
• To implement multiple disease analysis used machine learning algorithms, tensorflow and
StreamLit API. Python pickling is used to save the model behaviour and python
unpickling is used to load the pickle file whenever required.
4. Motivation
• There are multiple techniques in machine learning that can in a variety of industries,
do predictive analytics on large amounts of data. Predictive analytics in healthcare is
a difficult endeavour, but it can eventually assist practitioners in making timely
decisions regarding patients' health and treatment based on massive data.
• Diseases like Breast cancer, diabetes, and heart-related diseases are causing many
deaths globally but most of these deaths are due to the lack of timely check-ups of the
diseases. The above problem occurs due to a lack of medical infrastructure and a low
ratio of doctors to the population.
• This Prediction System, using various parameters helps to overcome the above
problem of lack of medical infrastructure and a low ratio of doctors to population.
• It acts as an immediate disease detector helping the doctors to treat the patient as
early as possible.
5. Literature Review
AUTHORS, TITLE, JOURNAL INFERENCE PARAMETER
Multi Disease Prediction Model by
using Machine Learning and Flask API
Author
Akkem Yaganteeswarudu
• Multi disease prediction model is used
to predict multiple diseases at a time.
• Here based on the user input disease
will be predicted.
• The choice will be given to user. If the
user want to predict particular disease
or if the user don't enter any disease
type then based on user entered inputs
corresponding disease model will be
invoked and predicted.
∙ parameters like :
∙ age, sex, bmi, insulin, glucose, blood pressure,
diabetes pedigree function, pregnancies,
considered in addition to age, sex, bmi,
insulin, glucose, blood pressure, diabetes
pedigree function, pregnancies are used
∙ Naive Bayes, Decision Tree, Random Forest is
used
Multiple Disease Prediction System
Author
Ankush Singh, Ashish Yadav, Saloni
Shah,Prof. Renuka Nagpure
• The main objective of this project was
to create a system that would predict
more than one disease and do so with
high accuracy.
• Because of this project the user doesn’t
need to traverse different websites
which saves time as well.
• Diseases if predicted early can
increase your life expectancy as well as
save you from financial troubles.
For the prediction the authors have used various
machine learning algorithms like
• Random Forest
• XGBoost
• K nearest neighbor (KNN) to achieve maximum
accuracy.
6. Literature Review
AUTHORS, TITLE, JOURNAL INFERENCE PARAMETER
Multi Disease Prediction
System
Author
K.M. Al-Aidaroos, A.A. Bakar, and Z. Othman
• For this study, the authors compared
Nave Baeyes to five other classifiers:
LR, KStar (K*), Decision Tree (DT),
Neural Network (NN), and a basic rule-
based algorithm (ZeroR).
• In the experiment, NB outperformed the
other algorithms in 8 of the 15 data
sets, leading to the conclusion that the
predictive accuracy results in Nave
Baeyes are superior to other
techniques.
∙ The criteria used in this study were age,
sex, smoking, being overweight, drinking
alcohol, blood sugar, heart rate, and blood
pressure. The risk level for various
parameters is saved with their ids ranging
from 1 to 100.
Diabetes Prediction using
Machine Learning
Techniques
Author
N. Joshi ,K.VijiyaKumar
• The proposed model gives the best
results for diabetic prediction and the
result showed that the prediction
system is capable of predicting the
diabetes disease effectively, efficiently
and most importantly, instantly.
• This project proposes an effective
technique for earlier detection of the
diabetes disease.
● The proposed approach uses various
classification and ensemble learning
method in which SVM, Knn, Random
Forest, Decision Tree, Logistic
Regression and Gradient Boosting
classifiers are used
7. Objectives
• The objective is to build a multi disease prediction system which will use real time
parameter such as glucose level, blood pressure value, Insulin level, BMI value to
predict the type of disease that person would likely be suffering at that particular point of
time.
• The model will learn the health conditions of a person, not only this, it will also learn
about the internal body parameters and will predict if the person is diseased according to
the features uploaded to the model.
• This will help to save person time as the model will learn the person’s body conditions
and will predict the if the person’s health is fine or not.
• The prediction system can also be used as a backend technologies and in other social
media app as a link to recommend the web application to the user
8. Design Methodology
• Dataset Collection (Real time data)
• Data Cleaning
• Data Preprocessing
1)Missing Values removal
2)Splitting of data
• Apply Machine Learning Algorithm
• Improving the accuracy
• Deploying the model as Web App
9. Project Plan
• Our plan is to collect the real parameters through various sources convert it into excel
sheet/csv file according to the parameters we have mentioned earlier.We can also
collect the data from various sites such as Kaggle, Saarbruecken etc.
• This will create our traning data set after which we will apply various data cleaning
methods to clean the data,after which we will process our data with the help of data
preprocessing methods.
• When the data is cleaned and processed we will train the data with help of various
Machine learning as well as Deep leaning algorithm and will choose the best performing
algorithm with high accuracy.
• When we will get the algorithm we will try to improve its accuracy by reducing the
errors and processing the data more.
• We will try to make and user iterface once the model starts predicting the data
accurately and deploy the model through User-Interface so that it can be used by
anyone.
10. Project Timeline
Review 1 Diabetes disease detection model to be shown for the
first review.
Review 2 Heart disease detection and Parkinson’s detection
model to be shown for the second review. The front
end to the web app is also shown.
Review 3 The fully functioning webapp to be presented for the
final review.
11. Requirements and Proposed Budget
As we are doing a machine learning based project so we will be using the free software's
only like Jupiter notebook, google colab and python and python libraries. All of these
requirements are free of cost and can be developed by just having a desktop/laptop.
Since We are using Open source software's there is no budget for this project
12. Project Deliverables
• Predict if the person is suffering from the particular disease.
• Input real-time parameters like glucose level, blood pressure value, Insulin level, BMI
value
• Analyse the parameters input by the user.
• Based on the parameters and body values we predict if the person is diseased.
• Develop the front end user interface to take input and predict.
13. Conclusion
The proposed work brings diabetes, heart disease, and Parkinson’s under
a single platform by deploying the trained models using the StreamLit
API framework which is a lightweight framework. Three classification
algorithms are used for training the models, in which the Support Vector
Machine gave good accuracy values for the disease prediction of diabetes
and Parkinson’s and Logistic Regression for the disease prediction of
heart disease.In the future, we can expand this work by adding more
diseases that are trained by machine learning models and also can include
the disease that involves deep learning models.
14. References
• [1] Naveen Kishore G,V .Rajesh ,A.Vamsi Akki Reddy, K.Sumedh,T.rajesh Sai Reddy, ”Prediction Of Diabetes Using
Machine Learning Classification Algorithms”.
• [2] Gavin Pinto, Sunil Jangid, Radhika Desai, ”Understanding the Lifestyle of people to identify the reasons of Diabetes
using data mining”.
• [3] M.Marimuthu ,S.Deivarani ,R.Gayatri, “Analysis of Heart Disease Prediction using Machine Learning Techniques”.
• [4] Purushottam, Richa Sharma ,Dr. Kanak Saxena, ”Efficient Heart Disease Prediction System”.
• [5] Adil Hussain She, Dr. Pawan Kumar Chaurasia,” A Review on Heart Disease Prediction using Machine Learning
Techniques”.
