XAI-HD: an explainable artificial intelligence framework for heart disease detection

• Published in: The Artificial intelligence review Vol. 58; no. 12; p. 385
• Main Authors: Talukder, Md. Alamin, Talaat, Amira Samy, Kazi, Mohsin, Khraisat, Ansam
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 17.10.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Blood pressure; Business metrics; Cardiovascular disease; Computer Science; Datasets; Decision making; Decision support systems; Deep learning; Electronic health records; Explainable artificial intelligence; Feature selection; Heart; Heart diseases; Machine learning; Medical prognosis; Missing data; Mortality; Optimization techniques; Performance evaluation; Prediction models; Rank tests; Real time; Risk assessment; Risk factors; Heart disease prediction; Healthcare AI applications; Explainable artificial intelligence (XAI); Data balancing techniques; SHAP and LIME interpretability; Machine learning (ML) and deep learning (DL)
• ISSN: 1573-7462; 0269-2821; 1573-7462
• DOI: 10.1007/s10462-025-11385-6

Cardiovascular disease (CVD) is the leading global cause of death, highlighting the urgent need for early, accurate, and interpretable diagnostic tools. However, many AI-based heart disease prediction models lack transparency, hindering their acceptance in clinical settings. This study proposes XAI-HD, a hybrid framework integrating machine learning (ML), deep learning (DL), and explainable AI (XAI) techniques for heart disease detection. The framework systematically addresses key challenges, including class imbalance, missing data, and feature inconsistency, through advanced preprocessing and class-balancing methods such as OSS, NCR, SMOTEN, ADASYN, SMOTETomek, and SMOTEENN. Comparative performance evaluations across multiple datasets (CHD, FHD, SHD) demonstrate that XAI-HD reduces classification error rates by 20–25% compared to traditional ML-based models, achieving superior accuracy, precision, recall, and F1-score. Additionally, SHAP and LIME-based feature importance analysis enhances model interpretability, fostering trust among medical professionals. The proposed framework holds significant real-world applicability, including seamless integration into hospital decision support systems, electronic health records (EHR), and real-time cardiac risk assessment platforms. Unlike conventional AI-driven cardiovascular risk prediction models, XAI-HD offers a more balanced, interpretable, and computationally efficient solution, ensuring both predictive accuracy and practical feasibility in clinical environments. Statistical validation using Wilcoxon signed-rank tests confirms the performance gains, and complexity analysis shows the framework is scalable for large-scale deployment.