Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models
Rattachement africain : Nigéria, ca. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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 "black boxes" 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's 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's 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.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models
- Date Crossref
- 16/09/2024
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Uyo Engineering and Mathematics (STEM) Centre University of Uyo, Nigéria (code pays fourni par la source)Université ou école supérieure
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University of Port Harcourt University of Port Harcourt, Nigéria (code pays fourni par la source)Université ou école supérieure
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Gregory University Uturu Nigéria (code pays fourni par la source)Université ou école supérieure
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University of Port Harcourt Teaching Hospital Port Harcourt, Nigéria (code pays fourni par la source)Établissement de santé
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University of Uyo Teaching Hospital University of Uyo Teaching Hospital, Nigéria (code pays fourni par la source)Établissement de santé
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Mount Royal University Department of Mathematics and Computing pays non établi dans la noticeUniversité ou école supérieure
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Ritman University Department of Mathematics and Computer Science Nigéria (pays nommé en fin d’affiliation)Université ou école supérieure
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Faculty of Computing Department of Computer Science University of Uyo, Nigéria (pays nommé en fin d’affiliation)Université ou école supérieure
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Ignatius Ajuru University of Education Department of Computer Science Port Harcourt, Nigéria (pays nommé en fin d’affiliation)Université ou école supérieure
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Faculty of Engineering Department of Computer Engineering Nigéria (pays nommé en fin d’affiliation)Université ou école supérieure
Engineering and Mathematics (STEM) Centre — University of Uyo (University of Uyo, Nigéria), University of Port Harcourt (University of Port Harcourt, Nigéria) et Gregory University Uturu (Nigéria), avec 7 autres affiliations. Pays d’affiliation : Nigéria.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.