A Comprehensive Analysis of Machine Learning Methods for Predicting Heart Disease

Heart disease is a global issue that significantly
impacts populations, highlighting the importance of early
detection for individual well-being. Machine Learning (ML)
techniques, such as those utilizing the Cleveland Heart Disease
dataset, offer cost-effective methods for accurately predicting
heart disease. This review focused on sharing the best practices of
several ML approaches in the context of patient prognosis,
diagnostics, and treatment of heart diseases. It begins with a Min-
Max Normalized Data Scaling (MMNDS) to preprocess the
dataset by addressing noise and missing values. Next, a Genetic-
Based Crow Search Algorithm (GCSA) identifies an optimal
feature subset. Finally, we employ Support Vector Machine
(SVM), Naive Bayes (NB), and Random Forest Classifier (RFC)
to predict heart disease based on clinical parameters.
Furthermore, the proposed classification methods provide
excellent results in identifying early heart diseases with
performance metrics such as error rate, precision, recall, F1-
score, and accuracy.