Open Access

ARTICLE

An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

Isha Kiran¹, Shahzad Ali^2,3, Sajawal ur Rehman Khan^4,5, Musaed Alhussein⁶, Sheraz Aslam^7,8,*, Khursheed Aurangzeb^6,*

1 Department of Computer Science, University of Engineering and Technology, Lahore, 39161, Pakistan
2 Dipartimento di Farmacia e Biotecnologie, Alma Mater Studiorum—Universitá di Bologna, Bologna, 40126, Italy
3 Department of Information Sciences, University of Education, Lahore, 54770, Pakistan
4 Department of Computer Science, National Textile University, Karachi Campus, Karachi, 74900, Pakistan
5 Department of Computer Science, National Textile University, Faisalabad, 37610, Pakistan
6 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
7 Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol, 3036, Cyprus
8 Department of Computer Science, CTL Eurocollege, Limassol, 3077, Cyprus

* Corresponding Authors: Sheraz Aslam. Email: ; Khursheed Aurangzeb. Email:

Computers, Materials & Continua 2025, 82(3), 5057-5078. https://doi.org/10.32604/cmc.2025.058724

Received 19 September 2024; Accepted 06 February 2025; Issue published 06 March 2025

Abstract

Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations, our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors, which is crucial for transparent and informed decision-making in healthcare. The framework uses ML techniques such as K-nearest neighbors (KNN), gradient boosting, random forest, and decision tree, trained on a cardiovascular dataset. Additionally, the integration of LIME and SHAP provides patient-specific insights alongside global trends, ensuring that clinicians receive comprehensive and actionable information. Our experimental results achieve 98% accuracy with the Random Forest model, with precision, recall, and F1-scores of 97%, 98%, and 98%, respectively. The innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability, fills a critical gap in existing approaches. This framework paves the way for more explainable and transparent decision-making in healthcare, ensuring that the model is not only accurate but also trustworthy and actionable for clinicians.

---

Keywords

---