Effective Prediction of Rheumatoid Arthritis (Ra) Diagnosis Using Hybrid Harmony Search with Adaptive Neuro Fuzzy Inference System

Abstract

One of the severe auto immune diseases that affects the entire human body is Rheumatoid Arthritis (RA), the disease triggers one’s immune system to attack the inner linings of bones and causes severe inflammation of the synovium. The continuous erosion of bone lining leads to permanent loss of the joint, accounting this severity the early prognosis of the disease is a significant and inevitable process. But, the sign and symptoms of the disease are always uncertain. The symptom of RA disease is similar to other inflammatory diseases, so highly experienced experts can identify the disease in its early stage. To support the clinicians and technicians for early prognosis of the disease, a computer-aided decision support model based on Harmony Search –Adaptive Neuro Fuzzy Inference System is presented in this study. The Harmony search algorithm is employed to select the optimal features, and ANFIS is adopted to perform classification. To demonstrate the effectiveness of the model, metrics such as Accuracy, Sensitivity, Specificity, Precision, Recall, F-measure, Positive Predictive Value, Negative Predictive Value, Root Mean Square Error, and Mean Absolute Error are employed and evaluated in MATLAB simulation environment. The proposed HS-ANFIS outperformed other models developed in this research and existing works of literature.