A Comparative Examination of Machine Learning Algorithms for Predicting Cardiovascular Disease

Over the previous few years, India’s heart disease incidence has varied between 1.2% and 13.2% in urban areas and 1.6% and 7.4% in rural areas. India would probably have more CVD deaths in 2020 which is 4.77 million compared to 2.26 million in 1990. Worldwide, 200 million individuals are estimated to be affected by heart disease. Heart disease affects over 110 million men and 80 million women globally. This study explores the use of machine learning (ML) with various models and data to predict cardiovascular illness with the goal of improving accuracy. Clinician parameters, lifestyle indicators, and demographic data are used to train and assess an assortment of machine learning methods, including Logistic Regression, Support Vector Machines, Random Forests, Decision Trees, and Naive Bayes.