Enhancing Cardiac Care: The Future of Heart Disease Prediction with Machine Learning

Abstract

Cardiovascular diseases (CVDs) have become increasingly prevalent and severe worldwide, necessitating a transition to more advanced and precise predictive models. Traditional approaches to the prognosis of coronary heart disease often lead to higher morbidity and mortality due to delayed early detection and inadequate individualized analysis. Machine learning (ML) techniques offer a groundbreaking solution, capable of analyzing vast amounts of patient data to provide early and accurate predictions of cardiac diseases. This shift holds great promise across various domains, including heart imaging, cardiothoracic surgery, cardiovascular anesthesiology, coronary critical care, pediatric cardiovascular disease, predictive cardiology, and interventional cardiology. In this comprehensive review, we evaluate several ML techniques, specifically focusing on classification and prediction algorithms, to assess their effectiveness in predicting cardiovascular diseases. We examine a range of algorithms such as K-Nearest Neighbors (K-NN), Decision Trees (DT), Support Vector Machines (SVM), Random Forests, Logistic Regression, XGBoost, Extra Trees Classifier, Gaussian Naive Bayes, and Artificial Neural Networks. By comparing these models, we aim to identify the most reliable and effective predictive tools for cardiovascular diseases. Notably, algorithms like Decision Trees and Extra Trees Classifiers have demonstrated remarkable performance, with recorded accuracies of 93.19% and 98.15%, respectively. This study underscores the importance of implementing machine learning approaches to enhance the accuracy and predictive capabilities in cardiac care. By leveraging these models, we can significantly improve early diagnosis, leading to better patient outcomes across cardiology specialties.