Learning fuzzy cognitive map with PSO algorithm for grading celiac disease

Abstract

Celiac disease (CD) is a complex disorder whose development is affected by genetics (HLA alleles) and gluten ingestion. Its diagnosis is very difficult due to clinical manifestations complexity, latent period, and similarity to other diseases. Studies show that a high percentage of CD patients remain undiagnosed. The celiac patients who are not treated are at a high risk of cancer, malignant lymphoma, and small-bowel neoplasia. Therefore, CD diagnosis and grading is of paramount importance. This paper presents a new method for grading CD based on the combination of fuzzy cognitive map (FCM) and support vector machine. To improve the efficiency and increase classification ability of FCM, particle swarm optimization (PSO) algorithm is applied to adjust FCM weights. In this study, the newest method of grading A, B1, and B2 is used. The empirical results show that the main advantage of PSO algorithm is its speed of convergence and the ability to obtain faster possible schedules. The proposed method is tested on 89 patients. The simulation results prove the superiority of the proposed method compared with Bayesian networks based on the rules and other procedures set forth in the literature. These results show the percentages of 87%, 86%, and 84% for three grades of A, B1 and B2.