Dynamic neutrosophic cognitive map with improved cuckoo search algorithm (DNCM-ICSA) and ensemble classifier for rheumatoid arthritis (RA) disease

Abstract

Rheumatoid Arthritis (RA) falls under the group of chronic autoimmune diseases, which affects the joints and muscles, and can lead to considerable damage to the joint structure and their functionality. RA diagnosis much in early stages is quite critical in stopping the progression of the disease. In this technical work, Dynamic Neutrosophic Cognitive Map with Improved Cuckoo Search Algorithm (DNCM-ICSA) with ensemble classifier is introduced for obtaining the gene expression profile, which distinguishes between the persons affected with RA and probable subjects of control. There are four important steps involved in this work, which include data preprocessing, feature selection, prediction and classification. The initial phase of the work comprises of data preprocessing, and second phase of the work comprises of gene selection process with T-test, chi-squared test, relief-F and Minimum Redundancy Maximum Relevance (mRMR). Next, the disease prediction is carried out using the ensemble mechanism, which increases the prediction accuracy. The ensemble mechanism integrates the process of Adaptive Neuro Fuzzy Inference System (ANFIS) and Deep Neural Networks (DNNs). The ensemble mechanism of classifiers is a group of classifiers whose decisions individually are integrated generally by weighted means for the classification of new RA examples. The disease of the patients may be avoided from getting to the severe stages. At last, DNCM-ICSA algorithm is utilized for gene classification. The results of the newly introduced classifier are analysed in terms of the metrics including precision, recall, F-measure and accuracy.