A Survey on Heart Disease Predictive Analysis...

A Survey on Heart Disease Predictive Analysis Using Machine Learning Algorithms

Author Name : Karthika G, Boopathi Kumar E

DOI: https://doi.org/10.56025/IJARESM.2025.1304251270

ABSTRACT Heart disease is still a prominent cause of global mortality. Heart disease detection earlier in life will improve survival significantly as well as quality of life. Routine diagnosis depends on keen clinical judgment, imaging, and invasive procedures but takes time and at times relies on human factor error. By knowing more about the patient, ML algorithms make prediction solutions about heart disease affordable with high levels of accuracy. This review focuses on the implementation of some of the many machine learning algorithms utilized in heart disease prediction, viz., Logistic Regression, Decision Tree, Random Forest, and SVM. These algorithms are used for prediction based on a variety of features like age, cholesterol, blood pressure, and chest pain type in heart disease prediction. Comparing their feature importance, cross-validation, and accuracy, we attempt to find out which are the most effective models for predicting heart disease. The review also briefly touches upon preprocessing the data, exploratory data analysis (EDA), interpretability problems of models, and ethical problems.