KNOWLEDGE BASED EXPERT SYSTEM ON COVID-19 DIAGNOSIS IN NIGERIA

Abstract

The rapid spread of the coronavirus (COVID-19) pandemic posed unprecedented challenges to
healthcare systems globally, particularly in developing nations like Nigeria. Limited access to
diagnostic tools, overburdened healthcare personnel, and geographic barriers to healthcare
delivery highlight the urgent need for innovative, scalable solutions. This study presents the
design and development of an expert system integrated within a telemedicine framework for the
early diagnosis of COVID-19 in Nigeria. The system leverages a rule-based approach, guided by
clinical guidelines from health authorities such as the WHO and NCDC, to assess patient-reported
symptoms and provide diagnostic suggestions. An expert system components framework was
designed from which the signs and symptoms of COVID-19 Virus were collected into the
knowledge base. The signs and symptoms of COVID-19 were collected through interview into
the knowledge base using MYSQL database. The system was developed using a lightweight
interface accessible via web platforms, the expert system offers an efficient, low-cost tool for
preliminary screening, especially in underserved and remote areas. The system was able to
correctly identify and classify user-reported symptoms (e.g., fever, cough, loss of taste/smell,
breathing difficulty) based on predefined diagnostic rules.