Effect of Using Grid Search Method on The Performance of Machine Learning Models for Heart Disease Classification

To get accurate data prediction results, it requires not only the right machine learning model, but also hyperparameters. Hyperparameters play an important role in shaping and optimizing the performance of machine learning models. Determining the right combination of model and hyperparameters is often a challenge. This research will use a hyperparameter search method, namely grid search. The aim of this research is to find out optimization best results from hyperparameters for several machine learning models using the Grid Search method and see the performance impact of using the grid search method for heart disease classification. Heart disease is a disease that has the potential to be fatal for humans if not treated quickly. Early detection can be done by utilizing machine learning models that are able to analyze data patterns from studied data sets. By training machine learning to study data sets related to heart disease, it is hoped that it will be able to carry out early detection of heart disease.