ENHANCED DENGUE DETECTION AND CONTROL USING A FUZZY EXPERT SYSTEM IN ENDEMIC REGIONS

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%.