2026-05-04T18:34:47.284Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:11838 2026-02-07T10:54:18Z 2

Cardiovascular Disease Risk Prediction Utilizing Two-Tier Classification Framework Optimized with Adapted Variable Neighborhood Search Algorithm 2026 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/11838 https://www.mdpi.com/1999-4893/19/2/130 S. John Villoth P. Dabic T. Zivkovic M. Zivkovic S. Andjelic M. Mravik V. Simic M. Abdel-Salam N. Bacanin Accurately assessing a patient’s 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’s 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. article 10.3390/a19020130 19 2:130 1 39 1999-4893 Algorithms