چكيده به لاتين
In medical diagnostic processes, the uncertainties that exist in medical information and linguistic expressions of physicians affect the performance of models developed for knowledge-based medical diagnosis systems. In order to deal with high degrees of uncertainty in this regard, a computer-aided medical diagnosis system is proposed in this research; which is a new extension of fuzzy cognitive map (FCM) based on type-2 fuzzy logic (called T2FCM). In T2FCM, type-2 fuzzy sets act as second-degree approximations of uncertainty by putting the uncertainties within a type-2 framework. In the proposed model, by designing a type-2 fuzzy logic system and using it to determine the initial value of weights, we were able to make the model robust against the high uncertainties that existed in the knowledge of experts and also against the hesitancy in determining the membership functions of fuzzy sets. In this work, the T2FCM model is designed for the field of pathology to classify the celiac disease (CD). CD is a self-immunity disorder and chronic disease of the small intestine, which is caused by dietary gluten (and especially some of its proteins called gliadins) in people who are genetically susceptible to this illness.
Also, a new method based on FCM has been proposed for predicting DNA-binding residues in a protein chain. For coding the existing residues in a protein sequence, three features have been used. To increase the classification accuracy, a new approach has been used to model the relative effects of each neighbor of a residue on that residue. For this purpose, by employing the sliding window and using the FCM, a central residue along with its neighbors have been mapped to a new one-dimensional space; and in this space, the relative effect of each residue has been modeled by means of a specific weight. It has been observed that the weights Wn are reduced by getting away from the central residue. Windows of various sizes have been examined and the prediction performance of each window has been evaluated based on the area under curve (AUC) measure; and it has been found that the optimal case occurs at the window size of 5. The AUC of 71.68 is obtained.
