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

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.