A Comparative Study of Machine Learning Models for Heart Disease Prediction Using Grid Search and Random Search for Hyperparameter Tuning

Abstract

An important global health concern is heart failure, for which early detection can greatly improve patient outcomes. Machine learning has proved to be useful in predicting the likelihood of heart disease by looking at factors like age, high blood pressure, and cholesterol. This study compares popular machine learning models, such as Random Forests, Gradient Boosting, Stacking, KNN, SVM, and Logistic Regression. We utilized a Grid Search as well as Random Search to improve the models' efficiency and perform-ability. Following model tuning, the models were determined using metrics like accuracy, recall, F1 score, and precision, AUC, Cohen's Kappa, and MCC. With grid search accuracy of 94.95% and random search accuracy of 94.54%, Random Forest produced the best results. This highlights how important it is to select the right model and adjust its parameters for the best results, and it also shows how well Random Forest predicts heart disease.