JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

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RDSSMM: A FUZZY EXPERT SYSTEM FOR ENHANCED MALARIA DIAGNOSIS AND MANAGEMENT IN ENDEMIC REGIONS

Authors

  • Muhammad Hamid
  • Saadia Malik
  • Muhammad Saleem

Keywords:

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

DOI:

https://doi.org/10.17654/0973514324028

Abstract

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.

Received: July 18, 2024
Revised: October 1, 2024
Accepted: October 10, 2024

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Published

2024-10-19

Issue

Vol. 24 No. 3 (2024)

Section

Articles

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Copyright (c) 2024 PUSHPA PUBLISHING HOUSE, PRAYAGRAJ, INDIA

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How to Cite

RDSSMM: A FUZZY EXPERT SYSTEM FOR ENHANCED MALARIA DIAGNOSIS AND MANAGEMENT IN ENDEMIC REGIONS. (2024). JP Journal of Biostatistics, 24(3), 517-526. https://doi.org/10.17654/0973514324028

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