{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:13:49Z","timestamp":1762514029105,"version":"build-2065373602"},"reference-count":83,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP26103523"],"award-info":[{"award-number":["AP26103523"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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 \u201cblack boxes,\u201d 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/a18110708","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T10:56:45Z","timestamp":1762513005000},"page":"708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Cardiovascular Disease Classification with Routine Blood Tests Using an Explainable AI Approach"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3039-6715","authenticated-orcid":false,"given":"Nurdaulet","family":"Tasmurzayev","sequence":"first","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4089-6337","authenticated-orcid":false,"given":"Bibars","family":"Amangeldy","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1919-3570","authenticated-orcid":false,"given":"Zhanel","family":"Baigarayeva","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"LLP Kazakhstan R&D Solutions, Almaty 050056, Kazakhstan"}]},{"given":"Assiya","family":"Boltaboyeva","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"LLP Kazakhstan R&D Solutions, Almaty 050056, Kazakhstan"}]},{"given":"Baglan","family":"Imanbek","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8525-8030","authenticated-orcid":false,"given":"Naoya","family":"Maeda-Nishino","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA 94305, USA"},{"name":"HAKUAI Medical Corporation, Osaka 573-1010, Japan"}]},{"given":"Sarsenbek","family":"Zhussupbekov","sequence":"additional","affiliation":[{"name":"Department of Automation and Control, Energo University, Almaty 050013, Kazakhstan"}]},{"given":"Aliya","family":"Baidauletova","sequence":"additional","affiliation":[{"name":"Neurology and Sleep Medicine Center, Almaty 050008, Kazakhstan"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2025). 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