Real-Time Dengue Risk Detection Using Fuzzy Logic Systems

Dengue fever is a mosquito-borne viral disease prevalent in tropical and subtropical regions, posing significant health risks, particularly when timely diagnosis and treatment are not administered. The disease is caused by the dengue virus (DENV) and transmitted primarily by Aedes aegypti and Aedes albopictus mosquitoes. If not detected and managed in its early stages, dengue fever can progress to severe dengue, leading to life-threatening complications such as haemorrhagic fever and dengue shock syndrome. This study presents a fuzzy logic-based framework for the early detection and prediction of dengue fever based on clinical symptoms reported by patients, like Body temperature, WBC count and Platelet count. By leveraging fuzzy logic, the proposed model generates a likelihood score indicating the probability of dengue infection. The rule-based fuzzy inference system effectively maps symptom severity to predefined linguistic variables, ensuring a more intuitive and flexible decision-making process. Experimental results demonstrate the model’s effectiveness in providing preliminary assessments, offering real-time symptom analysis, and issuing alerts for patients to seek medical attention promptly. By integrating fuzzy logic into dengue fever diagnosis, this system contributes to improved disease surveillance and timely treatment, ultimately mitigating the risks associated with delayed diagnosis.