A Data-Driven Intelligent Methodology for Developing Explainable Diagnostic Model for Febrile Diseases

Abstract

Febrile diseases such as malaria, typhoid fever, tuberculosis, and HIV/AIDS pose significant diagnostic challenges in Low- and Middle-Income Countries (LMICs). Misdiagnosis leads to delayed treatment, increased healthcare costs, and higher mortality rates. This study presents a prototype diagnostic framework integrating machine learning (ML) and explainable artificial intelligence (XAI) to enhance diagnostic performance, interpretability, and usability in resource-constrained settings. A dataset of 3914 patient records from secondary and tertiary healthcare facilities was used to train and validate predictive models, employing Random Forest, Extreme Gradient Boost, and Multi-Layer Perceptron with optimized hyperparameters. To ensure transparency, XAI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Large Language Models (LLMs) were integrated, enabling clinicians to understand model predictions. A prototype mobile-based diagnostic system was developed to explore its feasibility for real-time decision-making. The system features an intuitive interface, patient record management, and AI-driven diagnostic insights with visual and textual explanations. While usability testing with simulated case studies demonstrated its potential, real-world deployment and large-scale clinical validation are yet to be conducted. The system is designed with scalability in mind, allowing for future adaptation to different LMIC settings. However, limitations such as dataset imbalance and exclusion of pediatric data remain. Future research will focus on refining the model, expanding the dataset, and conducting extensive clinical validation before real-world implementation. This study serves as a foundational step toward AI-driven diagnostic tools in resource-limited healthcare environments.