XAI-HD: an explainable artificial intelligence framework for heart disease detection
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| Publié dans: | The Artificial Intelligence Review vol. 58, no. 12 (Dec 2025), p. 385 |
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| Autres auteurs: | , , |
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Springer Nature B.V.
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| Accès en ligne: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Talukder, Md. Alamin |u International University of Business Agriculture and Technology, Department of Computer Science and Engineering, Dhaka, Bangladesh (GRID:grid.443015.7) (ISNI:0000 0001 2222 8047) | |
| 245 | 1 | |a XAI-HD: an explainable artificial intelligence framework for heart disease detection | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Prediction models | ||
| 653 | |a Transparency | ||
| 653 | |a Datasets | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Medical records | ||
| 653 | |a Mortality | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Machine learning | ||
| 653 | |a Disease | ||
| 653 | |a Feature selection | ||
| 653 | |a Risk assessment | ||
| 653 | |a Business metrics | ||
| 653 | |a Missing data | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Support networks | ||
| 653 | |a Decision support systems | ||
| 653 | |a Feasibility | ||
| 653 | |a Medical prognosis | ||
| 653 | |a Blood pressure | ||
| 653 | |a Inconsistency | ||
| 653 | |a Medical personnel | ||
| 653 | |a Decision making | ||
| 653 | |a Frame analysis | ||
| 653 | |a Algorithms | ||
| 653 | |a Real time | ||
| 653 | |a Accuracy | ||
| 653 | |a Rank tests | ||
| 653 | |a Risk factors | ||
| 653 | |a Deployment | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Cardiovascular diseases | ||
| 653 | |a Heart diseases | ||
| 653 | |a Electronic health records | ||
| 653 | |a Medical decision making | ||
| 653 | |a Imbalance | ||
| 653 | |a Cardiovascular disease | ||
| 653 | |a Computerized medical records | ||
| 653 | |a Heart | ||
| 653 | |a Health records | ||
| 700 | 1 | |a Talaat, Amira Samy |u Electronics Research Institute, Computers and Systems Department, Cairo, Egypt (GRID:grid.463242.5) (ISNI:0000 0004 0387 2680) | |
| 700 | 1 | |a Kazi, Mohsin |u King Saud University, Department of Pharmaceutics, College of Pharmacy, Riyadh 11451, Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396) | |
| 700 | 1 | |a Khraisat, Ansam |u Deakin University, School of Information Technology, Burwood 3125, Australia (GRID:grid.1021.2) (ISNI:0000 0001 0526 7079) | |
| 773 | 0 | |t The Artificial Intelligence Review |g vol. 58, no. 12 (Dec 2025), p. 385 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
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