International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 4
July-August 2025

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A research on machine learning and explainable ai for cardiovascular disease prediction

Author(s) Sobana M, Ezhillarasi M V, Madhunika S, Ragavan M
Country India
Abstract Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved methods for early diagnosis and prevention. This study focuses on the development and comparison of four machine learning models-Logistic Regression, Random Forest, TabNet, and CatBoost to predict the risk of cardiovascular disease using structured clinical data. Furthermore, we implement SHAP (SHapley Additive Explanations) to provide interpretability and insight into each model's predictions. The dataset used comprises over 69,000 patient records with various clinical and lifestyle features. Among the models, CatBoost emerged as the top performer in terms of accuracy and AUC score. SHAP analysis revealed that features like age, systolic blood pressure, cholesterol, and weight significantly influenced the predictions. This study demonstrates the feasibility and utility of integrating explainable AI with predictive modeling in medical diagnostics, promoting transparency and trust in clinical decision-making.
Keywords Cardiovascular disease prediction, Machine learning, Explainable AI, CatBoost, TabNet, Random Forest, Logistic Regression, Model interpretability, Clinical decision support.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-30
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.51714
Short DOI https://doi.org/g9vpkj
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E-ISSN 2582-2160
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