Improved feature selection and classification for rheumatoid arthritis disease using weighted decision tree approach (REACT)

Rheumatoid arthritis (RA) is a major chronic autoimmune disorder which affects multiple organs and joints of human body. Disease varies in its behavior and concern such that an early prediction is a complex process with regard to time so the diagnosis is not an easy task for the physicians. The common existing methodologies employed to analyze the severity of RA are the clinical, laboratory and physical examinations. The advancement of data mining has been employed for the RA diagnosis through learning from history of datasets. To improve the efficiency and reliability of the approach, this paper presents a hybrid optimization strategy called REACT, which is based on the combination of the features of Iterative Dichotomiser 3 and Particle Swarm Optimization for feature selection and classification of RA. The effectiveness of the proposed diagnosis strategy is validated through its prediction accuracy, specificity, sensitivity, positive predictive value and negative predictive value with existing approaches.