The Heart of Artificial Intelligence: A Review of Machine Learning for Heart Disease Prediction

Heart disease is one of the main heart diseases that cause the death of people worldwide, affecting the engine of the human body: the heart. It has a greater incidence in underdeveloped countries such as Angola, Bangladesh, Ethiopia and Haiti, for this reason, obtaining accurate results based on risk factors manually is a complex task. Therefore, this systematic review allowed us to analyze and study 32 articles applying the PRISMA methodology, which allowed us to evaluate the suitability of the methods and, consequently, their reliability in the results. The results of the study showed that the algorithm with the greatest accuracy in predicting these heart diseases is Random Forest. The most commonly used metrics to evaluate machine learning algorithms are sensitivity, F1 score, precision, and accuracy, with sensitivity highlighted as the primary metric. The most predominant independent aspects for predicting heart disease in machine learning models are age, sex, cholesterol, diabetes, and chest pain. Finally, the most used data distribution is 70% for training and 30% for testing, which achieves great accuracy in the algorithm prediction process. This study offers a promising path for the prevention and timely treatment of this disease through the use of machine learning algorithms. In the future, these advances could be applied in a system accessible to all people, thus improving access to healthcare and saving lives.