2026-05-11T04:46:42.705Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:9352 2024-12-03T20:10:47Z 3

Tuning XGBoost by Planet Optimization Algorithm: An Application for Diabetes Classification, Chapter in LNEE Lecture Notes in Electrical Engineering: ICCCES 2022: Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems, Springer, volume 977 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9352 https://link.springer.com/chapter/10.1007/978-981-19-7753-4_60 L. Jovanovic M. Djuric M. Zivkovic D. Jovanovic I. Strumberger M. Antonijevic N. Budimirovic N. Bacanin Recent years have seen an increase in instances of diabetes mellitus, a metabolic condition that if left untreated can severely decrease the quality of life, and even cause the death of those affected. Early diagnostics and treatment are vital for improving the outcome of treatment. This work proposes a novel artificial intelligence-based (AI) approach to diabetes classification. Due to the ability to process large amounts of data at a relatively quick rate with admirable performance, the XGBoost approach is used. However, despite many advantages, the large number of control parameters presented by this algorithm makes the process of tuning delicate and complex. To this end, the planet optimization algorithm (POA) is tasked with selecting the optimal XGBoost hyperparameters so as to achieve the best possible classification outcomes. In order to demonstrate the improvements achieved, a comparative analysis is given that presents the proposed approach alongside other contemporary algorithms addressing the same classification task. The attained results clearly demonstrate the superiority of the proposed approach. bookPart Springer, Singapore 787 803 10.1007/978-981-19-7753-4_60 LNEE Lecture Notes in Electrical Engineering: ICCCES 2022: Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems, volume 977