Analysis of Heart Disease Prediction using Novel Machine Learning and Deep Learning Techniques

Abstract

Heart disease stands as one of the most prevalent causes of death globally, and India is no exception. In India, a considerable number of fatalities can be attributed to heart-related issues each year. Factors like high blood pressure, diabetes, smoking habits, and increasingly inactive lifestyles have further contributed to the growing cases of heart disease in the country. Identifying the disease at an early stage, as well as predicting its likelihood, plays a crucial role in enhancing patient outcomes and minimizing the overall healthcare burden.This research paper explores the development and evaluation of predictive models for heart disease using a dataset comprising various clinical and demographic features. Leveraging machine learning techniques, including Convolutional Neural Networks (CNN) and other models, we aim to identify key predictors of heart disease and develop an accurate model to assist healthcare professionals in early diagnosis and intervention. The study employs comprehensive data preprocessing, feature selection, and model evaluation to ensure robust and reliable predictions. Our findings highlight the potential of machine learning and deep learning models to significantly enhance heart disease prediction, thereby contributing to better management and prevention strategies in India.