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The migration of health workers from developing countries has resulted in frontline health workers (FHWs) using inadequate protocols for the diagnosis of complex health conditions. The study introduces an innovative AHP-based Medical Decision Support System (MDSS) incorporating disease risk factors derived from physicians\u2019 experiential knowledge to address this challenge. The system\u2019s aggregate diagnostic factor index determines the likelihood of febrile illnesses. Compared to existing literature, AHP models with risk factors demonstrate superior prediction accuracy, closely aligning with physicians\u2019 suspected diagnoses. The model\u2019s accuracy ranges from 85.4% to 96.9% for various diseases, surpassing physicians\u2019 predictions for Lassa, Dengue, and Yellow Fevers. The MDSS is recommended for use by FHWs in communities lacking medical experts, facilitating timely and precise diagnoses, efficient application of diagnostic test kits, and reducing overhead expenses for administrators.<\/jats:p>","DOI":"10.1177\/14604582241260659","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T06:43:29Z","timestamp":1718088209000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-criteria decision analysis method for differential diagnosis of tropical febrile diseases"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0189-737X","authenticated-orcid":false,"given":"Daniel E.","family":"Asuquo","sequence":"first","affiliation":[{"name":"Department of Information Systems, Faculty of Computing, University of Uyo, Uyo, 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