Chronic Kidney Disease Diagnosis Using Conditional Variational Generative Adversarial Networks and Squirrel Search Algorithm

Abstract

Globally, the prevalence of chronic kidney disease (CKD) is steadily increasing.  Computer-aided automated diagnostic (CAD) methods play a significant part in predicting CKD. Due to their highly effective classification accuracy, CAD systems like deep learning algorithms are essential in diagnosing diseases. This research creates an innovative categorization model with a metaheuristic algorithm based on the best characteristic selection to diagnose chronic kidney disease. Data with the absence of values were first removed during the pre-processing phase. Then, the optimal assortment of attributes is chosen using the Squirrel Search algorithm, a metaheuristic method that aids in more precise disorder prediction or categorization. Conditional Variational Generative Adversarial Networks were suggested for classification to identify the presence of CKD. Performance measures such as accuracy, precision, recall, and F1 score were evaluated on the benchmark CKD dataset to determine the efficiency of the suggested feature selection-based classifier. According to the experimental findings, the proposed method outperformed existing classification models with accuracy, precision, recall, and F1 score values of 99.2%, 98.4%, 98.6%, and 98.9%, respectively.