{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T02:02:59Z","timestamp":1785895379340,"version":"3.56.0"},"reference-count":87,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurately assessing a patient\u2019s likelihood of developing cardiovascular conditions is essential for proper case classification and for ensuring timely, targeted medical intervention. To address this need, the present study employs a carefully optimized machine learning framework to predict such risks within cardiology settings. A hybrid architecture is proposed that combines convolutional neural networks (CNNs) with cutting-edge gradient boosting classifiers, namely CatBoost and LightGBM, whose performance is further enhanced by metaheuristic optimization. The system adopts a two-layer design capable of capturing complex data structures while supporting accurate classification of cardiac patients and their risk of developing cardiovascular disease. Extensive evaluation on real-world data confirms the framework\u2019s effectiveness for binary classification, with the best models reaching an accuracy of slightly over 92%. To complement predictive performance, explainable AI methods were applied to clarify model decisions, yielding practical insights that can guide future data collection strategies and improve diagnostic precision.<\/jats:p>","DOI":"10.3390\/a19020130","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T15:24:19Z","timestamp":1770305059000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Cardiovascular Disease Risk Prediction Utilizing Two-Tier Classification Framework Optimized with Adapted Variable Neighborhood Search Algorithm"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7106-667X","authenticated-orcid":false,"given":"Saramma John","family":"Villoth","sequence":"first","affiliation":[{"name":"University of Technology and Applied Sciences, Nizwa 611, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2962-8480","authenticated-orcid":false,"given":"Petar","family":"Dabic","sequence":"additional","affiliation":[{"name":"Institute for Cardiovascular Diseases Dedinje, Heroja Milana Tepica 1, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2969-1709","authenticated-orcid":false,"given":"Tamara","family":"Zivkovic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4351-068X","authenticated-orcid":false,"given":"Miodrag","family":"Zivkovic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3069-6702","authenticated-orcid":false,"given":"Svetlana","family":"Andjelic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5442-3998","authenticated-orcid":false,"given":"Milos","family":"Mravik","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5709-3744","authenticated-orcid":false,"given":"Vladimir","family":"Simic","sequence":"additional","affiliation":[{"name":"Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11010 Belgrade, Serbia"},{"name":"Faculty of Engineering, Dogus University, Umraniye, 34775 Istanbul, T\u00fcrkiye"},{"name":"Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9714-0717","authenticated-orcid":false,"given":"Mahmoud","family":"Abdel-Salam","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information Science, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-924X","authenticated-orcid":false,"given":"Nebojsa","family":"Bacanin","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia"},{"name":"Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai 602105, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990\u20132019: Update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J. 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