Comprehensive Comparison of Different Machine Learning Algorithms for Heart Disease Prediction

The predicted efficacy of several machine learning algorithms for the identification of heart disease is examined in this research study. Six models were trained using a dataset that included clinical and demographic variables like blood pressure, cholesterol, age, sex, and more: Naive Bayes, Random Forest, K-Nearest Neighbours (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting (XGBoost), and XGBoost (XGB). Notably, with a remarkable accuracy of 99.9%, Random Forest and XGB were the best performers. These methods of ensemble learning proved to be more effective in identifying intricate patterns in the data and making precise predictions about the existence or absence of heart disease. Our research highlights the potential of machine learning to transform the diagnosis and risk assessment of cardiovascular disease, opening the door to more efficient and individualised healthcare solutions.