## 1 Introduction  
In tropical and Low- and Middle-Income Countries (LMICs), febrile diseases characterized by fever and frequently accompanied by other systemic symptoms present significant diagnostic and treatment challenges. Malaria, typhoid fever, urinary tract infections, Human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), respiratory tract infections, and tuberculosis are among the illnesses that greatly increase morbidity and mortality, particularly in LMICs. These febrile diseases often present overlapping symptoms which results in misdiagnosis and suboptimal treatments, which prolongs illness, raises healthcare expenses, and in extreme situations, can lead to death [1, 2, 3]. Thisemphasizes the need for trustworthy and interpretable diagnostic tools to aid healthcare professionals in making timely and efficient decisions. The Advancements in Artificial intelligence (AI) and machine learning (ML) have transformed the healthcare sector by providing data-driven insights into disease management, diagnosis, and treatment [4; 5]. These technologies use massive datasets to find relationships and patterns that conventional diagnostic techniques might miss [6]. In healthcare diagnostics, data-driven approaches are becoming increasingly significant because they offer improved efficiency and precision such as in medical diagnosis [7; 8; 9; 10; 11], personalized medicine, and healthcare analytics [12; 13; 14]. They rely on medical data collection, processing, and analysis to identify trends, aid clinical decision-making, and enhance patient outcomes [15]. ML techniques such as unsupervised, supervised, and reinforcement learning can be applied to develop medical diagnostic systems. However, while ML models like Random Forest (RF), Extreme Gradient Boost (XGBoost), and Multi-Layer Perceptron (MLP) algorithms have demonstrated impressive predictive capabilities, they are often criticized for their \"black-box\" nature [16; 17; 18]. This \"black-box\" nature hinders their acceptance in crucial healthcare applications where transparency is essential by making it difficult for clinicians to comprehend the reasoning behind their predictions [19]. Explainable AI (XAI) has addressed this gap by enhancing the interpretability of making ML models without sacrificing their predictive capabilities [20]. Frameworks like Large Language Models (LLMs) and Local Interpretable Model-Agnostic Explanations (LIME) offer visual representations and textual outputs that make model decisions easy to understand, especially by non-experts [21; 22]. Combining these frameworks allows diagnostic models to provide valuable insight, clarifying complex algorithm decisions and fostering professional trust. This strategy is important for febrile diseases, where early diagnosis and medical attention are essential for favorable patient outcomes. This study aims to develop a data-driven, intelligent methodology for building explainable and accurate diagnostic models for febrile diseases, integrating ML algorithms with interpretability frameworks to enhance clinical decision-making and promote transparency in AI-driven healthcare systems. The study employs a data-driven approach, using a rich dataset of patient records and symptoms to develop an explainable disease diagnostic model. ML algorithms like Random Forest, XGBoost, and Multi-Layer Perceptron can provide the basis for precise disease prediction, optimizing their performance through extensive hyperparameter tuning and validation techniques, and XAI techniques such as LIME can ensure that the predictions are transparent and give clinicians confidence they need to comprehend and trust the system. Large Language Models such as ChatGPT can offer explanations in natural language, improving interpretability and making the diagnostic process easier for patients and medical professionals to understand.