Malaria continues to be a pressing public health issue in tropical regions, with its deleterious effects on human health well documented. While a minority of cases may be life threatening, delayed diagnosis can significantly worsen the severity of malaria. This paper presents the Recommender and Decision Support System for Malaria Management (RDSSMM), designed to assist researchers, physicians and healthcare professionals in malaria endemic areas. RDSSMM is made up of four main components: a knowledge base, fuzzification module, inference engine, and defuzzification module. Mayo Hospital Lahore, Pakistan was used to develop the fuzzy expert system based on experts’ opinions in the field of medicine. For ease of access, a mobile application has been included which allows patients to get primary diagnosis by keying in their physical signs that are then transformed into fuzzy membership functions. The data was collected from the outpatient department of Mayo Hospital Lahore as one way of evaluating RDSSMM’s effectiveness. Comparing its predictions to expert medical diagnoses and reports pointed out this system’s performance. Malaria risk was correctly predicted 80% of the time by RDSSMM. These findings show that RDSSMM can greatly improve malaria management in rural areas through provision of quick and accurate results, which would facilitate timely decisions regarding malaria testing and monitoring.

Recommender and Decision Support System for Malaria Management (RDSSMM), fuzzification, defuzzification

Received: July 18, 2024; Revised: October 1, 2024; Accepted: October 10, 2024; Published: October 19, 2024

How to cite this article: Muhammad Hamid, Saadia Malik and Muhammad Saleem, RDSSMM: a fuzzy expert system for enhanced malaria diagnosis and management in endemic regions, JP Journal of Biostatistics 24(3) (2024), 517-526. https://doi.org/10.17654/0973514324028

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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