Abstract: Heart disease continues to be one of the foremost causes of death globally, posing significant challenges to healthcare systems. Early diagnosis is crucial for effective treatment and prevention, yet traditional diagnostic methods are often time- consuming, expensive, and dependent on the expertise of medical professionals. With the increasing availability of healthcare data and advancements in machine learning, automated systems for disease prediction have become a promising area of research. This paper presents a heart disease prediction system that leverages machine learning algorithms to assess the risk of heart disease based on clinical parameters. The system uses the UCI Heart Disease dataset, which includes features such as age, sex, chest pain type, resting blood pressure, cholesterol level, and other vital signs. Multiple classification algorithms— including Logistic Regression, Support Vector Machine (SVM), and Random Forest—were applied and evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Among the models tested, Random Forest achieved the highest performance in terms of prediction accuracy. The results demonstrate the potential of machine learning techniques in enhancing early diagnosis and assisting healthcare professionals in clinical decision-making. The study also provides a comparative analysis of different algorithms and discusses the importance of feature selection, data preprocessing, and model tuning. Future work will focus on integrating deep learning models and real- time data from wearable devices to improve the robustness and applicability of the system in real- world scenarios.

| DOI: 10.17148/IARJSET.2025.125344