Application of fuzzy expert system method for early detection of dengue fever
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Published:
Mar 30, 2024
Keywords:
Dengue Fever, Diagnostic Accuracy, Early Detection, Fuzzy Expert System, Symptom Evaluation
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Abstract
The application of the Fuzzy Expert System method in the early detection of dengue fever offers a promising approach to improve diagnostic accuracy. This study aims to develop a system that can overcome the diversity of dengue fever symptoms and uncertainty in the diagnosis process. Using medical record data of patients who have confirmed DHF, the study designed fuzzy rules for symptom evaluation, resulting in more precise diagnostic outputs. The results indicate the system's success in identifying dengue cases with high sensitivity and good positive predictive value. These findings confirm the importance of FES technology in clinical practice, especially for controlling and preventing dengue fever in endemic areas. Continued research will test this system in a broader clinical scenario to ensure its effectiveness and practicality in diverse medical environments.
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Prayoga, A. E. ., Surorejo, S. ., Kurniawan, R. D. ., & Gunawan, G. (2024). Application of fuzzy expert system method for early detection of dengue fever. Journal of Intelligent Decision Support System (IDSS), 7(1), 35-41. https://doi.org/10.35335/idss.v7i1.217
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Bergerot, C. D., Philip, E. J., Bergerot, P. G., & Pal, S. K. (2020). Distress and quality of life among patients with advanced genitourinary cancers. European Urology Focus, 6(6), 1150–1154.
Ceconi, M., Ariën, K. K., & Delputte, P. (2023). Diagnosing arthropod-borne flaviviruses: non-structural protein 1 (NS1) as a biomarker. Trends in Microbiology.
Cheng, J. T. (2020). Dominance, prestige, and the role of leveling in human social hierarchy and equality. Current Opinion in Psychology, 33, 238–244.
Gasmi, A., Noor, S., Tippairote, T., Dadar, M., Menzel, A., & Bjørklund, G. (2020). Individual risk management strategy and potential therapeutic options for the COVID-19 pandemic. Clinical Immunology, 215, 108409.
Grundy, B. S., & Houpt, E. R. (2022). Opportunities and challenges to accurate diagnosis and management of acute febrile illness in adults and adolescents: A review. Acta Tropica, 227, 106286.
Hegde, S. S., & Bhat, B. R. (2022). Dengue detection: Advances and challenges in diagnostic technology. Biosensors and Bioelectronics: X, 10, 100100.
Hoyos, W., Aguilar, J., & Toro, M. (2021). Dengue models based on machine learning techniques: A systematic literature review. Artificial Intelligence in Medicine, 119, 102157.
Hussain-Alkhateeb, L., Rivera Ramirez, T., Kroeger, A., Gozzer, E., & Runge-Ranzinger, S. (2021). Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review. PLoS Neglected Tropical Diseases, 15(9), e0009686.
Khalaf, A. H., Xiao, Y., Xu, N., Wu, B., Li, H., Lin, B., Nie, Z., & Tang, J. (2023). Emerging AI technologies for corrosion monitoring in oil and gas industry: A comprehensive review. Engineering Failure Analysis, 107735.
Kour, H., Manhas, J., & Sharma, V. (2020). Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review. Artificial Intelligence Review, 53, 4651–4706.
Kulkov, I., Ivanova-Gongne, M., Bertello, A., Makkonen, H., Kulkova, J., Rohrbeck, R., & Ferraris, A. (2023). Technology entrepreneurship in healthcare: Challenges and opportunities for value creation. Journal of Innovation & Knowledge, 8(2), 100365.
Mallikharjuna Rao, K., Saikrishna, G., & Supriya, K. (2023). Data preprocessing techniques: emergence and selection towards machine learning models-a practical review using HPA dataset. Multimedia Tools and Applications, 82(24), 37177–37196.
Manchadi, O., Ben-Bouazza, F., & Jioudi, B. (2023). Predictive maintenance in healthcare system: a survey. IEEE Access.
Saibene, A., Assale, M., & Giltri, M. (2021). Expert systems: Definitions, advantages and issues in medical field applications. Expert Systems with Applications, 177, 114900.
Sangkaew, S., Ming, D., Boonyasiri, A., Honeyford, K., Kalayanarooj, S., Yacoub, S., Dorigatti, I., & Holmes, A. (2021). Risk predictors of progression to severe disease during the febrile phase of dengue: a systematic review and meta-analysis. The Lancet Infectious Diseases, 21(7), 1014–1026.
Sievers, B. L., Siegers, J. Y., Cadènes, J. M., Hyder, S., Sparaciari, F. E., Claes, F., Firth, C., Horwood, P. F., & Karlsson, E. A. (2024). "Smart markets": harnessing the potential of new technologies for endemic and emerging infectious disease surveillance in traditional food markets. Journal of Virology, e01683-23.
Stephenson, C. J., Coatsworth, H., Kang, S., Lednicky, J. A., & Dinglasan, R. R. (2021). Transmission potential of Floridian Aedes aegypti mosquitoes for dengue virus serotype 4: Implications for estimating local dengue risk. Msphere, 6(4), 10–1128.
Tsheten, T., Clements, A. C. A., Gray, D. J., Adhikary, R. K., Furuya-Kanamori, L., & Wangdi, K. (2021). Clinical predictors of severe dengue: a systematic review and meta-analysis. Infectious Diseases of Poverty, 10, 1–10.
Wang, W.-H., Urbina, A. N., Chang, M. R., Assavalapsakul, W., Lu, P.-L., Chen, Y.-H., & Wang, S.-F. (2020). Dengue hemorrhagic fever–A systemic literature review of current perspectives on pathogenesis, prevention and control. Journal of Microbiology, Immunology and Infection, 53(6), 963–978.
Wu, T., Wu, Z., & Li, Y. (2022). Dengue fever and dengue virus in the People's Republic of China. Reviews in Medical Virology, 32(1), e2245.
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