Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer

INTRODUCTION: Screening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50% of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer. OBJECTIVE: to develop a fuzzy expert system that can identify gastric cancer risk levels in individuals. METHODS: This system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert’s opinion. RESULTS: 50 case scenarios were used to evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert’s diagnosis. Results revealed that sensitivity was 92.1% and the specificity was 83.1%. CONCLUSIONS: The results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate.