Comparative Analysis of Machine Learning Methods for Multi-Year CVD Prediction

Abstract

Cardiovascular diseases (CVDs) significantly contribute to global mortality, and early detection is crucial for preventing severe complications and reducing mortality rates. Machine learning (ML) has emerged as a promising tool for predicting heart disease using various medical data sources. However, most studies have focused on predicting the risk of heart disease at a single point in time, and there is a need for a model that can predict the long-term risk of heart disease. This study aims to address this gap by comparing the performance of eight different ML algorithms for predicting heart disease over one, two, and three-year periods. The first experiment found that the decision tree machine learning technique was the most effective in terms of run-time speed compared to the other techniques. The second experiment utilized the decision tree model and found that it could predict CVD with high accuracy up to four years in advance.