• Version
• Download 14
• File Size 370.38 KB
• File Count 1
• Create Date 12 April 2025
• Last Updated 12 April 2025

Machine Learning Based Predictive Analysis of Heart Failure and Type 2 Diabetes

Authors:

Prof. S.V.Phulari1, Mahek Bhartiya2, Aditya Dhas3 , Pavan Gadave4, Ayush Gawai5

,1Prof.S.V.Phulari Computer Engineering & PDEA’s College Of Engineering 2Mahek Bhartiya Computer Engineering & PDEA’s College Of Engineering 3Aditya Dhas Computer Engineering & PDEA’s College Of Engineering

4Pavan Gadave Computer Engineering & PDEA’s College Of Engineering

5Ayush Gawai Computer Engineering & PDEA’s College Of Engineering

Abstract- Heart disease and type 2 diabetes are significant global health challenges, necessitating reliable diagnostic methods for effective management. Traditional approaches often fall short, leading to the exploration of machine learning techniques. In this study, we present a machine learning-based system tailored for predicting both conditions, leveraging extensive datasets. Employing machine learning algorithms, alongside feature selection and cross-validation techniques, our system demonstrates robust performance in identifying individuals at risk. Using Machine learning (ML) we have proposed and built the framework for prediction of heart disease and diabetes. A custom ensemble algorithm with hard and soft voting classifier is built for different cardiovascular datasets yielding accuracies in the range of 95.

Key Words: Heart failure, Cardiovascular diseases, Type-II Diabetes, Machine Learning.