ABSTRACT
In recent times, intelligent robots have gained the capability of transforming the role of a physician and revolutionizing the practice of medicine. Recent studies reported that robots can be used as an effective tool for disease diagnosis of several medical statuses as humans proficiently. In this view, this chapter presents an intelligent robot in the disease discovery process using a whale optimization-algorithm (WOA)-based feature selection (FS) with a fuzzy-rule based classifier (FRBC), named WOA-FRBC. The WOA-FRBC model involves three distinct stages, namely preprocessing, FS, and classification. In addition, the WOA-based FS process is applied to extract a useful set of features from the provided preprocessed data. Besides, the FRBC model is employed for classification purposes, which determine the proper class label of the applied data. In order to investigate the proficient results of the WOA-FRBC model, several experiments were performed. The experimental outcomes show that the WOA-FRBC model has a better diagnostic outcome than those of the earlier techniques. The WOA-FRBC model achieves effective results owing to the automated rule generation of the FRBC model and optimal subset selection by WOA.
