Heart disease remains one of the leading causes of mortality worldwide, emphasizing the need for early detection and preventive healthcare strategies. Traditional diagnostic methods often require invasive procedures and may fail to provide timely predictions. In this study, we propose a machine learningbased approach for heart disease prediction using clinical data such as age, gender, blood pressure, cholesterol levels, resting electrocardiographic results, maximum heart rate, and other medical attributes. Various classification algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), are employed and compared to evaluate their predictive performance. The dataset used for experimentation is preprocessed through normalization, feature selection, and handling of missing values. Evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are used to assess model effectiveness. The experimental results demonstrate that ensemble-based models achieve higher prediction accuracy compared to traditional methods. This work highlights the potential of machine learning techniques in developing intelligent healthcare systems capable of supporting physicians in early diagnosis and personalized treatment planning for patients at risk of heart disease.
Heart disease prediction, machine learning, clinical data, logistic regression, decision tree, random forest, support vector machine, K-nearest neighbors, healthcare analytics, medical diagnosis, classification algorithms, predictive modeling, feature selection, healthcare system.