Diagnosis of Celiac Disease using Fuzzy Logic Probabilistic System in North-Indian Patients

Abstract

ABSTRACT Introduction: Fuzzy logic is gaining popularity with predictive healthcare applications in the field of medicine. It is consistently being used in recognising various chronic diseases with optimum accuracy. Fuzzy logic is also supposed to be a beneficial tool to diagnose celiac disease with approximate reasoning. Aim: To design a fuzzy logic inference system to predict celiac disease based on individual symptoms in North-Indian patients. Materials and Methods: The study was conducted in Punjab, Northern India, on 700 individuals using a questionnaire stratagem. SPSS software was used for the analysis of celiac data. The fuzzy logic system was implemented in Python software with 6 inputs and 1 fuzzy output parameter to determine the probability level of celiac disease. Results: The fuzzy logic system produced 98.8% accuracy, 98.5% sensitivity and 98.39% specificity with minimal error rate. The prevalence of celiac disease obtained a 1:19 ratio from the survey in Punjab with 134 celiac patients. Conclusion: Fuzzy logic yields better results in terms of sensitivity and specificity in comparison with other existing disease probabilistic systems. Hence, the fuzzy system is a fruitful tool for celiac disease disclosure without any painful testing strategy.

Keywords

• body mass index
• prediction
• weight loss