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The medical field is challenged by accurately diagnosing these intertwined diseases of coexisting ailments and anticipating their rise. The current diagnostic approaches are time-consuming and inaccurate, hinder effective treatment, and delay accurate results for the patient. Artificial intelligence can provide an effective method for early prediction of comorbidity risks. In this study, various artificial intelligence models are used, and a clinical dataset of 271 patients is utilized to diagnose comorbidity. In which a hybrid diagnosis model is proposed based on the intersection between machine learning (ML) and feature selection techniques for the detection of comorbidity. Fuzzy decision by opinion score method is utilized as a sophisticated tool to select the most representative ML for prediction. Extensive simulation results showed an accuracy rate of 91.463 using AdaBoost ML. Furthermore, utilizing the fuzzy decision by opinion score technique, we were able to confirm that the best model using all features as well as the chi square and KBest features is the AdaBoost, which scored the smallest value of 0.204 and hence confirm that it is the best selected ML model for comorbidity.<\/jats:p>","DOI":"10.1515\/jisys-2024-0418","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T09:21:01Z","timestamp":1741771261000},"source":"Crossref","is-referenced-by-count":2,"title":["Comorbidity diagnosis using machine learning: Fuzzy decision-making approach"],"prefix":"10.1515","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3312-2581","authenticated-orcid":false,"given":"Dheyauldeen M.","family":"Mukhlif","sequence":"first","affiliation":[{"name":"National School of Electronics and Telecommunications of Sfax (ENET\u2019Com), University of Sfax , Sfax, 4868 , Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0548-0616","authenticated-orcid":false,"given":"Dhafar Hamed","family":"Abd","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, University of Anbar , Ramadi, 31001 , Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8148-1621","authenticated-orcid":false,"given":"Ridha","family":"Ejbali","sequence":"additional","affiliation":[{"name":"Research Team in Intelligent Machines (RTIM), University of Gabes , Gabes, 4868 , Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0642-3384","authenticated-orcid":false,"given":"Adel M.","family":"Alimi","sequence":"additional","affiliation":[{"name":"Research Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax , Sfax, 4868 , Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7855-6030","authenticated-orcid":false,"given":"Mohammed Fadhil","family":"Mahdi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Al-Mansour University College , Baghdad, 10069 , Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8413-0045","authenticated-orcid":false,"given":"Abir Jaafar","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Sharjah , Sharjah, 27272 , United Arab Emirates"}]}],"member":"374","published-online":{"date-parts":[[2025,3,12]]},"reference":[{"key":"2025122009032222369_j_jisys-2024-0418_ref_001","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Olmos L, Salvador CH, Alberquilla A, Lora D, Carmona M, Garcia-Sagredo P, et al. 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Projections of multi-morbidity in the older population in England to 2035: estimates from the Population Ageing and Care Simulation (PACSim) model. Age Ageing. 2018;47(3):374\u201380. 10.1093\/ageing\/afx201.","DOI":"10.1093\/ageing\/afx201"},{"key":"2025122009032222369_j_jisys-2024-0418_ref_005","doi-asserted-by":"crossref","unstructured":"Khalaf M, Hussain AJ, Alafandi O, Al-Jumeily D, Alloghani M, Alsaadi M, et al. An application of using support vector machine based on classification technique for predicting medical data sets. Intel Comput Theor Appl. 2019;15:580\u201391. 10.1007\/978-3-030-26969-2_55.","DOI":"10.1007\/978-3-030-26969-2_55"},{"key":"2025122009032222369_j_jisys-2024-0418_ref_006","doi-asserted-by":"crossref","unstructured":"Khalaf M, Hussain AJ, Keight R, Al-Jumeily D, Keenan R, Chalmers C, et al. 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