## 4 Conclusions  
This study successfully developed a prototype of a data-driven and explainable diagnostic model for febrile diseases, integrating machine learning algorithms with an Explainable AI technique and a Large Language Model. The model demonstrated good predictive performance, with Random Forest achieving the highest diagnostic performance and interpretability. By incorporating LIME for feature attribution and ChatGPT for textual  
Figure 13: Explainable diagnosis page.  
explanations, the system will enhance clinical decision-making and increase user trust among healthcare providers. The prototype system was designed to be scalable and adaptable, making it particularly relevant for resource-limited settings where access to expert medical diagnosis is constrained. The model's potential mobile deployment will offer front-line healthcare workers a tool for rapid disease screening and decision support, especially in rural and underserved communities. By simulating expert reasoning, the system aims to assist and not replace medical professionals in making timely, evidence-based diagnoses. To ensure practical implementation, several steps are recommended:  
* Pilot testing in clinical environments--A real-world evaluation is necessary to assess the model's usability, effectiveness, and integration within existing healthcare workflows;
* Dataset expansion--Including more diverse populations, particularly pediatric patients under five years old, and incorporating additional febrile diseases such as hemorrhagic fevers will enhance generalizability;
* Handling imbalanced data--Diseases like tuberculosis and HIV/AIDS had fewer records in the dataset, affecting model performance. Future iterations should explore data augmentation or ensemble techniques to address this limitation;
* Continuous model updates--The system should be retrained periodically with new patient data to reflect emerging disease trends and improve diagnostic accuracy over time;
* Mobile and cloud deployment--Deploying the model via mobile health (mHealth) apps will facilitate accessibility, particularly in low-resource regions where medical infrastructure is limited.  
Despite its strengths, the study acknowledges key limitations. The prototype has not yet been deployed, and its real-world effectiveness has yet to be validated. Additionally, while LIME and ChatGPT enhanced interpretability, further refinements are needed to reduce biases and improve the clarity of model explanations. Long-term validation in clinical settings will be essential to establish its reliability and usability. This study highlights the transformative potential of combining ML, XAI, and LLMs to address the diagnostic challenges of febrile diseases, particularly in Low- and Middle-Income Countries. By emphasizing transparency, scalability, and real-world applicability, the system represents a step forward in AI-assisted healthcare. Future work should focus on bridging the gap between research and real-world deployment, ensuring that the model can be effectively integrated into clinical practice to improve global health outcomes.  
Conceptualization, F.-M.U., C.A. and K.F.A.; methodology, K.F.A., C.A. and F.-M.U.; validation, F.-M.U., K.F.A. and C.A.; formal analysis, K.F.A.; data curation, K.F.A.; writing--original draft preparation, K.F.A., C.A. and F.-M.U.; writing--review and editing, K.F.A., C.A. and F.-M.U. supervision, F.-M.U. and C.A.; project administration, F.-M.U. funding acquisition, F.-M.U. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the New Frontier Research Fund, grant number NFRFE-2019-01365 between April 2020 and March 2024.  
Not applicable.  
The data used in this study are publicly available at [[https://doi.org/10.5281/zenodo.13756418](https://doi.org/10.5281/zenodo.13756418)]([https://doi.org/10.5281/zenodo.13756418](https://doi.org/10.5281/zenodo.13756418)).  
The authors declare no conflicts of interest.