Enhancing Cardiovascular Disease Classification with Routine Blood Tests Using an Explainable AI Approach
Tasmurzayev N. Amangeldy B. Baigarayeva Z. Boltaboyeva A. Imanbek B. Maeda-Nishino N. Zhussupbekov S. Baidauletova A.
November 2025Multidisciplinary Digital Publishing Institute (MDPI)
Algorithms
2025#18Issue 11
Background: While machine learning (ML) is widely applied in cardiology, a critical research gap persists. The incremental diagnostic value of routine blood tests for classifying cardiovascular disease (CVD) remains largely unquantified, and many models operate as non-interpretable “black boxes,” limiting their clinical adoption. This study aims to address these gaps by quantifying the contribution of readily available laboratory panels and demonstrating the utility of transparent diagnostic modeling within a real-world clinical cohort. Methods: We conducted a retrospective study on the clinical data of 896 adult patients from a hospital database. A baseline feature set (demographics, vital signs) was compared against an enhanced set that additionally included results from routine hematology and biochemistry panels. Five machine learning classifiers were trained and evaluated. To ensure transparency, SHAP (SHapley Additive exPlanations) analysis, a key component of explainable AI (XAI), was used to interpret the predictions of the top-performing model. Results: The inclusion of routine blood tests consistently and significantly improved the performance of all classifiers. The XGBoost model demonstrated the best performance (accuracy 91.62%, precision 95.00%, recall 87.36%). Critically, SHAP analysis identified aspartate aminotransferase (AST), glucose, and creatinine as the most significant biomarkers, providing clear, interpretable insights into the biochemical drivers of the model’s predictions. Conclusion: Routine laboratory markers contain a strong, interpretable signal indicative of CVD that is crucial for accurate risk stratification. These findings underscore the diagnostic relevance of common blood biomarkers and demonstrate how explainable AI can transform routine clinical data into transparent and actionable cardiovascular insights. Further validation in larger and demographically diverse cohorts is warranted.
blood biomarkers , cardiovascular disease , diagnostic classification , explainable AI , feature importance , hematological markers , machine learning , personalized medicine , risk stratification , SHAP
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Faculty of Information Technologies and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
LLP Kazakhstan RD Solutions, Almaty, 050056, Kazakhstan
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, 94305, CA, United States
HAKUAI Medical Corporation, Osaka, 573-1010, Japan
Department of Automation and Control, Energo University, Almaty, 050013, Kazakhstan
Neurology and Sleep Medicine Center, Almaty, 050008, Kazakhstan
Faculty of Information Technologies and Artificial Intelligence
LLP Kazakhstan RD Solutions
Department of Psychiatry and Behavioral Sciences
HAKUAI Medical Corporation
Department of Automation and Control
Neurology and Sleep Medicine Center
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