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Explainable Artificial Intelligence for Cardiovascular Disease Risk Stratification Using Electronic Health Records.

Sam M. K.
Volume 5

Abstract

Cardiovascular disease (CVD) remains one of the most critical public health challenges worldwide, requiring early identification of high-risk patients to reduce adverse outcomes such as myocardial infarction, stroke, heart failure, and cardiovascular-related mortality. Traditional risk prediction tools, including rule-based scoring systems, provide valuable clinical guidance but often have limited ability to capture complex nonlinear relationships among heterogeneous patient variables stored in Electronic Health Records (EHRs). Moreover, the adoption of advanced machine learning models in cardiovascular medicine is frequently constrained by their black-box nature, which limits clinician trust, transparency, and practical deployment in real-world healthcare settings. This paper proposes an Explainable Artificial Intelligence (XAI)-driven framework for cardiovascular disease risk stratification using structured EHR data, integrating predictive accuracy with clinically interpretable decision support.

The proposed framework utilizes demographic information, vital signs, laboratory test results, medication history, comorbidities, lifestyle-related risk factors, and diagnostic codes extracted from EHR systems to predict patient-specific cardiovascular risk categories, including low-risk, moderate-risk, and high-risk groups. A comprehensive preprocessing pipeline is applied to address missing values, normalize numerical features, encode categorical variables, remove redundant attributes, and mitigate class imbalance using advanced resampling strategies. Several machine learning and deep learning models are investigated, including Random Forest, XGBoost, LightGBM, multilayer perceptron networks, and attention-based neural architectures, with hyperparameter optimization employed to enhance predictive performance and model generalization.

To improve transparency and clinical interpretability, the proposed framework incorporates explainability techniques such as SHAP, LIME, feature importance analysis, and patient-level risk contribution visualization. These methods identify the most influential predictors contributing to cardiovascular risk, including age, systolic blood pressure, cholesterol level, diabetes status, smoking history, body mass index, prior cardiac events, renal function markers, and inflammatory biomarkers. By providing both global explanations for population-level risk patterns and local explanations for individual patient predictions, the framework enables clinicians to understand not only which patients are at elevated risk but also why a specific risk score has been assigned.

The framework is designed for evaluation using publicly available and real-world EHR datasets, such as MIMIC-IV and cardiovascular clinical registries, with model performance assessed through accuracy, sensitivity, specificity, F1-score, calibration analysis, and AUC-ROC. Experimental evaluation is expected to demonstrate that the proposed explainable model outperforms conventional statistical risk scoring approaches and non-explainable machine learning baselines while maintaining clinically meaningful interpretability. The proposed system offers a scalable, transparent, and clinically actionable decision-support tool for early cardiovascular risk stratification, supporting preventive care, personalized intervention planning, and efficient allocation of healthcare resources. Its explainable design makes it particularly suitable for integration into hospital information systems and routine clinical workflows, where trust, accountability, and interpretability are essential for responsible AI adoption.

 

Keywords

References

  • [1] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Abdelhamid, A. A., Eid, M. M., & Ibrahim, A. (2024). Greylag goose optimizati on:
  • [2] Abdollahzadeh, B., Khodadadi, N., Barshandeh, S., Trojovský, P., Gharehchopogh, F. S., El -kenawy, E. S. M., ... & Mirjalili, S. (2024).
  • [3] El-Kenawy, E. S. M., Ibrahim, A., Mirjalili, S., Eid, M. M., & Hussein, S. E. (2020). Novel feature selection and voting classif ier
  • [4] El-Kenawy, E. S. M., Eid, M. M., Saber, M., & Ibrahim, A. (2020). MbGWO -SFS: Modified binary grey wolf optimizer based on
  • [5] El-Kenawy, E. S., & Eid, M. (2020). Hybrid gray wolf and particle swarm optimization for feature selection. Int. J. Innov. Compu t. Inf.
  • [6] El-Kenawy, E. S. M., Mirjalili, S., Ibrahim, A., Alrahmawy, M., El -Said, M., Zaki, R. M., & Eid, M. M. (2021). Advanced meta -
  • [7] Khodadadi, N., Abualigah, L., El -Kenawy, E. S. M., Snasel, V., & Mirjalili, S. (2022). An archive -based multi -objective arithmetic
  • [8] Ibrahim, A., Mirjalili, S., El -Said, M., Ghoneim, S. S., Al -Harthi, M. M., Ibrahim, T. F., & El -Kenawy, E. S. M. (2021). Wind speed
  • [9] Abdelhamid, A. A., El -Kenawy, E. S. M., Alotaibi, B., Amer, G. M., Abdelkader, M. Y., Ibrahim, A., & Eid, M. M. (2022). Robust
  • [10] El-Kenawy, E. S. M., Mirjalili, S., Alassery, F., Zhang, Y. D., Eid, M. M., El -Mashad, S. Y., ... & Abdelhamid, A. A. (2022). Novel
  • [11] Hassib, E. M., El -Desouky, A. I., Labib, L. M., & El -Kenawy, E. S. M. (2020). WOA+ BRNN: An imbalanced big data classification
  • [12] Eid, M. M., El -kenawy, E. S. M., & Ibrahim, A. (2021, March). A binary sine cosine -modified whale optimization algorithm for feature
  • [13] El-Sayed Towfek, M. (2018). El -kenawy. Trust Model for Dependable File Exchange in Cloud Computing. International Journal of
  • [14] Abdelhamid, A. A., Towfek, S. K., Khodadadi, N., Alhussan, A. A., Khafaga, D. S., Eid, M. M., & Ibrahim, A. (2023). Waterwhee l plant
  • [15] El-Kenawy, E. S. M., Abdelhamid, A. A., Ibrahim, A., Mirjalili, S., Khodadad, N., Alduailij, M. A., ... & Khafaga , D. S. (2023). Al -
  • [16] Alhussan, A. A., Abdelhamid, A. A., El -Kenawy, E. S. M., Ibrahim, A., Eid, M. M., Khafaga, D. S., & Ahmed, A. E. (2023). A binary
  • [17] Alhussan, A. A., Khafaga, D. S., Abotaleb, M., Mishra, P., & El -Kenawy, E. S. M. (2024). Global Potato Production Forecasting Based
  • [18] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [19] Mishra, P., Alhussan, A. A., Khafaga, D. S., Lal, P., Ray, S., Abotaleb, M., ... & El -Kenawy, E. S. M. (2024). Forecasting production of
  • [20] El-Kenawy, E. S. M., Mirjalili, S., Abdelhamid, A. A., Ibrahim, A., Khodadadi, N., & Eid, M. M. (2022). Meta -heuristic optimization
  • [21] Abdelhamid, A. A., El -Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., ... & Saber, M. (2022).
  • [22] Alharbi, A. H., Towfek, S. K., Abdelhamid, A. A., Ibrahim, A., Eid, M. M., & Khafaga, D. S. & Saber, M.(2023). Diagnosis of
  • [23] Alhussan, A. A., & Towfek, S. K. (2024). 5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algori thm.
  • [24] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [25] Abdelhamid, A. A., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Eed, M. (2024). Potato harvesting pred iction
  • [26] Eed, M., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Arnous, R. (2024). Potato Consumption Forecastin g
  • [27] Mahmood, S., Sun, H., Iqbal, A., Alhussan, A. A., & El -kenawy, E. S. M. (2024). Green finance, sustainable infrastructure, and green
  • [28] El-Kenawy, E. S. M., Alhussan, A. A., Khodadadi, N., Mirjalili, S., & Eid, M. M. (2024). Predicting potato crop yield with machi ne
  • [29] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [30] Ghasemi, M., Khodadadi, N., Trojovský, P., Li, L., Mansor, Z., Abualigah, L., ... & El -Kenawy, E. S. M. (2025). Kirchhoff’s law
  • [31] Yassen, M. A., El -Kenawy, E. S. M., Abdel -Fattah, M. G., Ismail, I., & Mostafa, H. E. D. S. (2025). Explainable artificial intelligence
  • [32] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [33] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [34] El-kenawy, E. S. M., Alhussan, A. A., Mattar, E. A., & Radwan, M. (2026). Feature selection and hyperparameter tuning in transfo rmer -
  • [35] Chen, L., Xu, C., Lim, W. H., Sharma, A., Tiang, S. S., Chong, K. S., ... & Khafaga, D. S. (2025). Transparent and reliable c onstruction
  • [36] Khodadadi, N., Towfek, S. K., Zaki, A. M., Alharbi, A. H., Khodadadi, E., Khafaga, D. S., ... & Eid, M. M. (2025). Predicting
  • [37] Alhussan, A. A., El -Kenawy, E. S. M., Khafaga, D. S., Alharbi, A. H., & Eid, M. M. (2025). Groundwater resource prediction and
  • [38] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [39] Lopes, J. M., Pinho, C. S., Martins, R. C., & Bandeira, T. (2026). Sustainability Gets Smarter: Competitive Pressure and AI L eading the
  • [40] Radwan, M., Ibrahim, A., Abdelsalam, M. M., Alhussan, A. A., Mattar, E. A., & El -Kenawy, E. S. M. (2026). Optimizing solar and wind
  • [41] Alhussan, A. A., El -Kenawy, E. S. M., Eid, M. M., & Khodadadi, N. (2026). Hybrid Al -Biruni and Puma Optimization (BERPO) for
  • [42] El-Kenawy, E. S. M., Ibrahim, A., Alhussan, A. A., Khafaga, D. S., Ahmed, A. E., & Eid, M. M. (2026). Smart city electricity loa d
  • [43] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Zaki, A. M., Ibrahim, A., Alhussan, A. A., ... & Eid, M. M. (2026). Glider sn ake
  • [44] Mekaret, F., Rabehi, A., Zebentout, B., Tizi, S., Douara, A., Bellucci, S., ... & Alhussan, A. A. (2024). A comparative study of Schottky
  • [45] KOUADRI, Ali, RABEHI, Abdelhalim, BENZIANE, Ali, et al. A Robust Multi -Transform Watermarking Scheme for Medical Images
  • [46] Tibermacine, I. E., Russo, S., Scarano, G., Tedesco, G., Rabehi, A., Alhussan, A. A., ... & Napoli, C. (2025). Conditional VA E for
  • [47] Kouadri, A., Benziane, A., Rabehi, A., Rabehi, A., Alhussan, A. A., Khafaga, D. S., & El -Kenawy, E. S. M. (2025). A novel hybrid
  • [48] Russo, S., Tibermacine, I. E., Randieri, C., Rabehi, A., Alharbi, A. H., El -Kenawy, E. S. M., & Napoli, C. (2025). Exploiting facial
  • [49] Bentegri, H., Rabehi, M., Kherfane, S., Nahool, T. A., Rabehi, A., Guermoui, M., ... & El -Kenawy, E. S. M. (2025). Assessment of
  • [50] Tibermacine, I. E., Russo, S., Citeroni, F., Mancini, G., Rabehi, A., Alharbi, A. H., ... & Napoli, C. (2025). Adversarial de noising of EEG
  • [51] Ouahabi, M. S., Benyounes, A., Barkat, S., Ihammouchen, S., Rekioua, T., Rabehi, A., ... & Alharbi, A. H. (2025). Real -time sensor fault
  • [52] Belaid, A., Guermoui, M., Khelifi, R., Arrif, T., Chekifi, T., Rabehi, A., ... & Alhussan, A. A. (2024). Assessing suitable a reas for PV
  • [53] Mehallou, A., M’hamdi, B., Amari, A., Teguar, M., Rabehi, A., Guermoui, M., ... & Khafaga, D. S. (2025). Optimal multiobjecti ve
  • [54] Rabehi, A., El -Hadi, M., Benmahmoud, S., Rabehi, A., Alharbi, A. H., & El -Kenawy, E. S. M. (2025). SOCA -CFAR Processor in A
  • [55] Bakria, D., Beladel, A., Korich, B., Teta, A., Mohammedi, R. D., Laouid, A. A., ... & El -kenawy, E. S. (2025). A novel enhanced Grey
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