This paper proposes an expert system for detecting dengue using a fuzzy logic approach in Pakistan. A knowledge-based system represents an expert system, which is one of the most frequent types of Artificial Intelligence in Medicine (AIM), with medical knowledge of a clearly defined goal and the ability to reach the correct conclusion. In a proposed system, the knowledge of a particular issue is typically represented by a set of rules rather than individual variables. Through mosquito bites an infected mosquito transmits the dengue virus that functions as a pathogen exclusively in human bodies. Dengue fever is an infectious tropical disease. The risk of dying from dengue fever increases when the diagnosis is delayed, despite the fact that only a small fraction of people infected with the disease actually develop severe symptoms. Because of this, it is essential to diagnose dengue fever in its earliest stages. As a result, the main purpose of this research was to construct an expert system for the early detection of dengue disease utilizing the Fuzzy Inference System (FIS), a potent instrument for coping with imprecision and uncertainty. The system takes a patient’s physical symptoms as input and translates them into fuzzy membership functions for analysis. The system that was designed can be used to assist a patient in receiving an early diagnosis of dengue disease. The proposed system has been tested on real data sets and achieved a remarkable accuracy rate of 96%.

artificial intelligence in medicine, disease detection, dengue fever, fuzzy sets, fuzzy inference system

Received: December 4, 2024; Accepted: February 14, 2025; Published: February 14, 2025

How to cite this article: Muhammad Saleem, Saadia Malik, Muhammad Hamid and Mohammed Aman, Enhanced dengue detection and control using a fuzzy expert system in endemic regions, JP Journal of Biostatistics 25(2) (2025), 243-258. https://doi.org/10.17654/0973514325012

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

References:
[1] M. Carbone, J. Lednicky, S.-Y. Xiao, M. Venditti and E. Bucci, Coronavirus 2019 infectious disease epidemic: where we are, what can be done and hope for, Journal of Thoracic Oncology 16 (2021), 546-571.
[2] F. Zeshan, A. Ahmad, M. I. Babar, M. Hamid, F. Hajjej and M. Ashraf, An IoT- enabled ontology-based intelligent healthcare framework for remote patient monitoring, IEEE Access 11 (2023), 133947-133966.
[3] W. Chen, J. Li, J. Li, J. Zhang and J. Zhang, Roles of non-coding RNAs in virus-host interaction about pathogenesis of hand-foot-mouth disease, Current Microbiology 79 (2022), 1-9.
[4] S. Rajendran, S. Giridhar, S. Chaudhari and P. K. Gupta, Technological advancements in occupational health and safety, Measurement: Sensors 15 (2021), 100045.
[5] X. Qian and S. V. Ukkusuri, Connecting urban transportation systems with the spread of infectious diseases: A trans-SEIR modeling approach, Transportation Research Part B: Methodological 145 (2021), 185-211.
[6] R. A. Almihyawi, Z. T. Naman, H. M. Al-Hasani, Z. T. Muhseen, S. Zhang and G. Chen, Integrated computer-aided drug design and biophysical simulation approaches to determine natural anti-bacterial compounds for Acinetobacter baumannii, Scientific Reports 12 (2022), 6590.
[7] Y. W. Kerk, K. M. Tay and C. P. Lim, Monotone fuzzy rule interpolation for practical modeling of the zero-order TSK fuzzy inference system, IEEE Transactions on Fuzzy Systems 30 (2021), 1248-1259.
[8] Y. W. Kerk, C. Y. Teh, K. M. Tay and C. P. Lim, Parametric conditions for a monotone TSK fuzzy inference system to be an n-ary aggregation function, IEEE Transactions on Fuzzy Systems 29 (2020), 1864-1873.
[9] E. A. Ibrahim, D. Salifu, S. Mwalili, T. Dubois, R. Collins and H. E. Z. Tonnang, An expert system for insect pest population dynamics prediction, Computers and Electronics in Agriculture 198 (2022), 107124.
[10] W. Hoyos, J. Aguilar and M. Toro, A clinical decision-support system for dengue based on fuzzy cognitive maps, Health Care Management Science 25(4) (2022), 666-681.