A Grey Wolf and Rough Set Hybrid Approach for the Detection of Chronic Kidney Disease

In the currently expanding population, there are numerous methods for enhancing health, including prior disease detection, medication, and diagnosis. Consequently, acquiring knowledge is crucial at various phases of decisions because of the ambiguity present in the decision system. The prime objective is to deal with ambiguity while choosing attributes for decision-making. Besides, classification, clustering, and decision rule generation while handling uncertainties is another challenge. Swarm optimization is an effective method to identify important characteristics in addressing real-life problems. On the other side, a rough set aids the decision system in producing rules for decisions. This research presents an integrated approach that employs the rough set and grey wolf optimization techniques to evaluate real-life information systems while managing ambiguities. The proposed technique finds the optimal features employing a hybridized rough set and grey wolf optimization technique and further develops the decision support system using a rough set. The proposed method is evaluated over chronic kidney disease decisions to show its effectiveness while generating a decision support system.