EXPERT SYSTEM FOR EARLY DIAGNOSIS OF STROKE

ABSTRACT

Stroke is a medical emergency that needs prompt attention because it stops the supply of blood to the brain. Early detection of the disease accurately, depends on the approach/method utilized in diagnosing the disease. As such, a suitable method that can accurately detect stroke becomes a compelling alternative to overcome the challenges peculiar to the disease. An expert system for early diagnosis of stroke is proposed to ameliorate the challenges because it is an intelligent information processing system that can aid physicians in managing the uncertainties associated with stroke aid and diagnosis. This project work designed and implemented an expert system for early diagnosis of stroke that uses the human-like reasoning style of Fuzzy Logic to diagnose and suggest possible treatments for stroke through interactivity with user. With aim of developing an expert system and exploring the potential of fuzzy logic to assist clinicians in Nigeria to accurately predict and differentiate between the different types of stroke. It employs programs like MySQL, PHP, JAVA and XML while tools like WEKA (Waikato Environment for Knowledge Analysis) and WampServer, were used to integrate these techniques together. The system proved to be of enormous advantage in diagnosing stroke, as it diagnoses and learns about each user per time, to provide adequate and appropriate results and also makes reliable predictions to users.