{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T01:23:01Z","timestamp":1794360181299,"version":"4.9.0"},"reference-count":56,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"New Frontier Research Fund","award":["NFRFE-2019-01365"],"award-info":[{"award-number":["NFRFE-2019-01365"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["TropicalMed"],"abstract":"<jats:p>Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as \u201cblack boxes\u201d with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model\u2019s decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system\u2019s performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets.<\/jats:p>","DOI":"10.3390\/tropicalmed9090216","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T07:36:00Z","timestamp":1726472160000},"page":"216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2199-5049","authenticated-orcid":false,"given":"Kingsley","family":"Attai","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, Ritman University, Ikot Ekpene 530101, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6774-5259","authenticated-orcid":false,"given":"Moses","family":"Ekpenyong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computing, University of Uyo, Uyo 520103, Nigeria"},{"name":"Science, Technology, Engineering and Mathematics (STEM) Centre, University of Uyo and Centre for Research, University of Uyo, Uyo 520103, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Constance","family":"Amannah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Ignatius Ajuru University of Education, Port Harcourt 500102, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Asuquo","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computing, University of Uyo, Uyo 520103, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peterben","family":"Ajuga","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Gregory University, Uturu 441106, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Okure","family":"Obot","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computing, University of Uyo, Uyo 520103, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekemini","family":"Johnson","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Ritman University, Ikot Ekpene 530101, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anietie","family":"John","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Ritman University, Ikot Ekpene 530101, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omosivie","family":"Maduka","sequence":"additional","affiliation":[{"name":"University of Port Harcourt Teaching Hospital, Port Harcourt 500102, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christie","family":"Akwaowo","sequence":"additional","affiliation":[{"name":"University of Uyo Teaching Hospital, Uyo 520103, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faith-Michael","family":"Uzoka","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computing, Mount Royal University, Calgary, AB T3E 6K6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ceh.2024.05.001","article-title":"Febrile Disease Modeling and Diagnosis System for Optimizing Medical Decisions in Resource-Scarce Settings","volume":"7","author":"Asuquo","year":"2024","journal-title":"Clin. eHealth"},{"key":"ref_2","first-page":"5334813","article-title":"Malaria and Typhoid Fever Coinfection among Febrile Patients in Ngaound\u00e9r\u00e9 (Adamawa, Cameroon): A Cross-Sectional Study","volume":"2023","author":"Ymele","year":"2023","journal-title":"J. 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