Predicting Heart Disease through Machine Learning Methods

Heart diseases including heart attacks, cause about 31% of global deaths, remaining a significant health threat despite preventability. Limited tech advancements and awareness, especially in developing nations, amplify this challenge. Machine learning offers promise in tackling this issue, with studies advocating ensemble methods for accurate predictive models. These models analyze extensive medical data to efficiently predict heart diseases, undergoing stages like data exploration, feature selection, model implementation, and comparative analysis. A model using Logistic Regression, Naive Bayes, and Random Forest initially identified top-performing models, later refined to CatBoost, RandomForest, and XGBoost through cross-validation and tuning. A hybrid model, combining Logistic Regression, CatBoost, and RandomForest, achieved a 97% accuracy, showcasing improved precision, recall, F1 score, and ROC AUC. This underscores machine learning's potential in enhancing predictive accuracy and refining strategies to combat heart diseases effectively.