Md Jobayer Rahman, Shamim Ahamed, Fahim Faisal Deepto, Marzia Zaman, Ashraful Islam, Farhana Sarker, Khondaker A Mamun
2025 IEEE International Conference on Data Mining Workshops (ICDMW)
In: 2025 IEEE International Conference on Data Mining Workshops (ICDMW)
IEEE, pp. 568-575
Healthcare systems in low- and middle-income countries (LMICs) are overwhelmed and incapable of accommodating increasing demand. Millions of people turn to self-medication without proper guidance, which often results in delayed diagnosis and adverse health outcomes. Many common medications then lose their effectiveness due to improper use. Such an environment creates a critical need for accessible and reliable AI-driven systems to guide patients toward appropriate care. To address these challenges, we developed and compared two distinct approaches, an ensemble machine learning (ML) model and a fine-tuned large language model (LLM). To develop the systems, we curated and clinically validated a large-scale dataset of patient cases. Both frameworks were designed to provide personalized health recommendations at the point of care. In a subsequent real-world evaluation against expert physician consensus, the ML model's recommendations achieved 94.6 % accuracy, while the LLM achieved 81 %. Although the ML model demonstrated higher predictive performance, the LLMs showed a stronger understanding of the context in unstructured narratives. These findings provide the foundational evidence for a hybrid health triage system that synergistically combines the narrative interpretation of LLMs with the predictive reliability of ML. Such a system can create a more robust, safe, and trustworthy tool for patient health risk assessment, delivering scalable, clinically validated, and low-cost guidance to significantly enhance affordable healthcare access and improve health equity.
Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh (IUB)Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact