Design of Pattern Recognition System for the Diagnosis of Gonorrhea Disease

Sexually transmitted diseases STDs share common symptoms and can be classified as confusable disease as such become difficult for physicians to correctly diagnose them. This work develops a pattern recognition system for the diagnosis of gonorrhea disease using genetic algorithm. Data on gonorrhea symptoms are collected and used in the development of the knowledge base. We classify the membership grade of the symptoms based on the mean of maxima and the derived membership function. The system accepts symptoms as input and provides the degree of membership of each symptom in any gonorrhea symptoms sets. We develop our system using PHP programming tool as back end and Java as front end platform. We explore Ms Access for the design of our database. The model helps the physicians to identify gonorrhea disease by its symptoms and provides solid basis for possibly determination of the ailment exactly if there are all symptoms.