{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:14:09Z","timestamp":1780413249495,"version":"3.54.1"},"reference-count":84,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Diabetes mellitus is a highly prevalent chronic disease; early diagnosis reduces severe complications. This work presents a diabetes prediction pipeline that combines metaheuristic feature selection with machine learning classification. We propose a hybrid Particle Swarm Optimization and Grey Wolf Optimizer (PSO-GWO) with alternating collaboration and an adaptive fitness function that adjusts to class balance, sample size, and dimensionality. Selected features are evaluated with random forest (primary), support vector machines, k-nearest neighbors, and logistic regression. The approach is assessed on three clinical datasets (Pima Indians, Frankfurt Hospital, Iraq) using stratified five-fold cross-validation. At the feature selection stage, the hybrid selector reaches 83.36% mean cross-validation accuracy while retaining about 74% of features on average. At the final classification stage, after random forest hyperparameter optimization on the selected features, the optimized random forest achieves 84.74% mean accuracy. Feature count is reduced by about 26% on average without loss of performance, improving interpretability and prospects for clinical use.<\/jats:p>","DOI":"10.3390\/info17060533","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:38:42Z","timestamp":1780411122000},"page":"533","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Diabetes Prediction System Based on Hybrid PSO-GWO Feature Selection and Optimized Machine Learning"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8368-6358","authenticated-orcid":false,"given":"Amine","family":"Ziane","sequence":"first","affiliation":[{"name":"LIMED Laboratory, Faculty of Exact Sciences, University of Bejaia, Bejaia 06000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houda El","family":"Bouhissi","sequence":"additional","affiliation":[{"name":"LIMED Laboratory, Faculty of Exact Sciences, University of Bejaia, Bejaia 06000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5636-1660","authenticated-orcid":false,"given":"Thomas","family":"Hanne","sequence":"additional","affiliation":[{"name":"Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021). 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