Heart disease risk prediction using machine learning model

Ayankoya, Folasade Yetunde and Olaogun, Tamilore Kayode and Udosen, Alfred Akpan and Ogunsusi, Adetunji Gabriel (2025) Heart disease risk prediction using machine learning model. Global Journal of Engineering and Technology Advances, 24 (2). 036-049. ISSN 2582-5003

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.