Enhancing the Prediction of Heart Disease using Machine Learning Algorithms

Heart disease represents one of the top causes of death in different parts of the world, indicating a need for better detection as well as more effective risk assessments of this problem at an earlier stage. Here, this paper looks into various machine learning methods to predict a person's risks of heart diseases by considering the seven most imperative algorithms: Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Random Forest, and Adaptive Boosting. By reviewing studies and datasets, we assess how well these algorithms work for heart disease prediction. Our findings reveal that machine learning models have significantly improved the accuracy of predicting heart disease, in comparison to traditional methods. Among all the above-mentioned algorithms, Random Forest has given the best results, with some studies reporting up to 93.44% in identifying potential heart disease cases. The study ends with the finding that machine learning algorithms are able to be really effective at the prediction of heart disease and great potential for actual use, improve patient care and help reduce the global impact of heart disease. Keywords: Cardiovascular Risk Prediction, Machine Learning Algorithms, Random Forest, Support Vector Machine, Predictive Analytics in Healthcare, Early Disease Detection.