Diagnosis of Autoimmune Hepatitis with High-Order Fuzzy Cognitive Map

Abstract

In this paper, we provide a novel technique based on a high-order fuzzy cognitive map (HFCM) to predict autoimmune hepatitis (AIH). The basic features that are extracted by specialists are used as the input concepts of the HFCM model. Particle swarm optimization (PSO) algorithm is used to enhance the capability and increase the efficiency of HFCM classification. In order to evaluate the performance, our method is applied to 216 patients. In this paper, we have also used the chaotic PSO (CPSO) algorithm; which, as extensions of PSO algorithm, improve the performance of PSO in terms of global optimality, reliability, convergence speed and solution accuracy. The results of applying different CPSOs are compared with classical PSO. The best results in this case, which are achieved by applying the CPSO, are 85.71%, 86.21% and 87.88% for the definite, probable and improbable classes, respectively. Therefore, the highest grading accuracies are achieved by using the combination of fourth order learned HFCM by CPSO.