Classification of Heart Failure Using Machine Learning: A Comparative Study

Heart disease continues to be one of the leading causes of mortality worldwide, making early and accurate diagnosis essential for effective treatment and prevention. In recent years, machine learning techniques have gained significant attention in healthcare applications due to their ability to analyze complex medical data and support clinical decision-making. This paper presents a comprehensive heart disease prediction framework based on a comparative analysis of seventeen machine learning models, including AdaBoost, Random Forest, Support Vector Machine, Extra Trees, Logistic Regression, Ridge Classifier, XGBoost, Passive Aggressive Classifier, Linear Discriminant Analysis, Gradient Boosting, Stacking, Bagging, Naïve Bayes, k-Nearest Neighbors, Stochastic Gradient Descent, Quadratic Discriminant Analysis, and Decision Tree.