Iraq's Major Infectious Disease Diagnosis Using A Fuzzy Rule-Based System

Abstract

The World Health Organization (WHO) declared that over 15 million people die annually due to infectious diseases at an average of 50,000 deaths per day. In the past two centuries, about 30 new cases have emerged and are threatening the health of millions of people currently. For most of these diseases, there is no available treatment strategy or vaccine currently. The WHO report warned that some of the major infectious diseases such as malaria, cholera, and hepatitis are a challenge in several countries despite the concerted efforts on their prevention and treatment. In this work, a fuzzy rule-based system was designed for the diagnosing of some major existing infectious diseases in Iraq and described the ways of detecting new ones. Diagnosis is often based on the symptoms that can be seen or felt. The medical advising system helps physicians or experts to make an appropriate disease diagnosis. Infection diseases have several symptoms and some of these symptoms are similar, thereby, creating several difficulties for accurate decision or diagnosis. In this study, the knowledge used was acquired through expert interviews and presented by the production rule (IF-THEN method). Fuzzy logic has been proven as a great tool for building intelligent decision-making knowledge-based systems and expert feedback. The results achieved in this study indicated a promising diagnosis performance of the system as it achieved 85.7% F-Score and 82.3% accuracy. This system will help in saving many lives in the rural communities where health care centers and expert physicians are limited.