000168615 001__ 168615 000168615 005__ 20260211123813.0 000168615 0247_ $$2doi$$a10.1007/s10489-025-07081-1 000168615 0248_ $$2sideral$$a147956 000168615 037__ $$aART-2026-147956 000168615 041__ $$aeng 000168615 100__ $$0(orcid)0000-0002-7998-5476$$aTrillo, José Ramón$$uUniversidad de Zaragoza 000168615 245__ $$aExplainable classifier with adaptive optimisation for medical data 000168615 260__ $$c2026 000168615 5060_ $$aAccess copy available to the general public$$fUnrestricted 000168615 5203_ $$aArtificial Intelligence (AI) has become increasingly important in critical domains such as medicine, where accurate and interpretable decision-making is essential. However, many high-performing AI models operate as “black boxes”, limiting transparency and making it difficult for clinicians to understand or verify predictions. To address this challenge, we present an eXplainable Artificial Intelligence (XAI) framework that integrates a fuzzy rule-based classifier with genetic algorithms and 2-tuple linguistic representations. The method incrementally generates general fuzzy rules, introduces fuzzy exception rules to capture atypical cases, and applies rule selection and parameter tuning to enhance both accuracy and interpretability. Experiments on nine medical datasets demonstrate that our approach achieves competitive or superior accuracy compared to state-of-the-art algorithms, while requiring fewer rules. These results show that the method not only improves predictive performance but also provides clear, human-readable explanations for each decision, thereby increasing trust and facilitating its application in medical practice. 000168615 536__ $$9info:eu-repo/grantAgreement/ES/MICIU/PID2022-139297OB-I00 000168615 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es 000168615 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000168615 700__ $$aDel Moral, María José 000168615 700__ $$aTapia, Juan Miguel 000168615 700__ $$aGarcía-Cabello, Julia 000168615 700__ $$aCabrerizo, Francisco Javier 000168615 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000168615 773__ $$g56, 3 (2026), 77 [23 pp.]$$pAppl. intell.$$tAPPLIED INTELLIGENCE$$x0924-669X 000168615 8564_ $$s2815411$$uhttps://zaguan.unizar.es/record/168615/files/texto_completo.pdf$$yVersión publicada 000168615 8564_ $$s2250333$$uhttps://zaguan.unizar.es/record/168615/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000168615 909CO $$ooai:zaguan.unizar.es:168615$$particulos$$pdriver 000168615 951__ $$a2026-02-11-10:28:19 000168615 980__ $$aARTICLE