Heart disease risk prediction using machine learning model

Cardiovascular disease persists as a primary worldwide cause of mortality, highlighting the critical requirement for precise and available early diagnostic methods. This work creates a prediction system for heart disease employing three machine learning techniques: Logistic Regression, Random Forest, and Multilayer Perceptron (MLP). The study employs thorough preprocessing, feature selection, and hyperparameter optimization to enhance performance; utilizing the Cleveland Heart Disease dataset. The result revealed that MLP attained the best accuracy (88.0%), F1-score (89.3%), and AUC (88.4%), showcasing its excellent predictive ability and a well-balanced compromise between sensitivity and specificity. Unlike many prior studies, this work emphasizes real-world deployment, clinical usability, and model explainability. Hence, the final MLP model was deployed as a Streamlit web app, providing a user-friendly interface for clinicians and patients to assess heart disease risk based on inputted clinical data. The research highlights the ability of machine learning to improve preventive care and establishes a groundwork for future growth through larger datasets, interpretability tools, and practical clinical evaluations.