A Multiagent-Based Model for Tracing Symptoms of Infectious Diseases

Abstract

Symptoms associated with infectious diseases are very complex to detect and sometimes time-consuming to easily bring together by medical experts as most of them are very relative to the other being symptomatic and some being asymptomatic too. This has resulted in ineffective diagnosis that leads to pointing to one thing but later turns out to be another thing entirely as likened to the Covid-19 era, where symptom tracing was a major menace and unidentified infected persons could transmit to other people in a matter of split seconds. The aim of this research is to build a multiagent-based model to identify the symptoms of infectious diseases using fuzzy logic technique. The dataset used for the study was obtained from the University of Port Harcourt as well as the test set for the proposed system. The methodology adopted is Object-Oriented System Analysis and Design (OOSAD). The findings achieved reveal that contact tracing was done and performed efficiently. Further, by comparing the existing and proposed systems based on some performance metrics, including speed of user validation, number of methods adopted, number of algorithms used, number of machine learning techniques adopted, and number of databases used, it is evident that the new system performs better than the existing one, with speed in user validation now reduced to 11% value better than 32% in existing which obviously consumes more time. The study has been able to come up with an effective model that will help in dealing with uncertainty and the process of guessing in human health issues hence saving lives, time, and stress.