Track: Machine Learning

Abstract

The leading cause of death in the world is heart disease. For medical professionals, prediction is challenging because it requires a higher level of predictive expertise. There is a knowledge gap in the field of healthcare despite the abundance of information. Information is either present but not mined in underdeveloped nations like Zimbabwe, or it is present but not mined to gain insights. The data gathered over time by the healthcare industry can help Artificial Intelligence (AI) technologies produce accurate predictions and decision-making outcomes. In this study, we used the Random Forest algorithm as our experimental model to evaluate AI's performance in predicting the risk of developing heart disease. With a recall of 91% and an F1-score of 83 percent, we were able to predict heart disease with an accuracy of 80% using a random forest algorithm. Thus, the most effective algorithm for classifying heart disease has been found to be the random forest algorithm.