Predicting rheumatoid arthritis from the biomarkers of clinical trials using improved harmony search optimization with adaptive neuro-fuzzy inference system

Abstract

Rheumatoid Arthritis (RA) is a chronic autoimmune disease whose symptoms are hard to determine due to the overlapping indications of the condition with other illnesses such as dengue, malaria, etc. As the symptoms of RA disease are similar to inflammatory diseases, general physicians (GPs) find it difficult to detect the disease earlier. A computer aided framework is proposed in this study to assist and support the GPs to diagnose RA better. In this work Improved Harmony Search Optimization (IHSO) approach is proposed to select the significant feature subset of RA and Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as a classification model. The performance of the proposed IHSO-ANFIS model is examined with metrics such as Balanced Accuracy (Bacc), Area under Curve (AUC), Sensitivity (Sen), Specificity (Spec), and Matthew’s Correlation Coefficient (MCC) using 10-Fold cross-validation. Additionally, the results of the IHSO-ANFIS are compared with HSO-ANFIS, ANFIS without any feature selection and standard bench mark datasets. IHSO-ANFIS attained 87.05% Bacc, 89.95% AUC and 0.6586 MCC on the RA dataset. From the results it is clear that IHSO-ANFIS could assist general physicians to diagnose RA earlier and pave the way for timely treatment.