Classification of Vital Genetic Syndromes Associated With Diabetes Using ANN-Based CapsNet Approach

Abstract

Diabetes has been linked to a wide range of genetic abnormalities or disorders like Cushing syndrome, Wolfram’s syndrome. The factual significance of these relatively uncommon disorders originates from the knowledge that supplies into the potential processes driving prevalent diabetes. Diabetes-related syndromes are presently classified based on clinical and biochemical characteristics. However, until now, no expertise classification strategies are developed for classifying diabetes-associated syndrome disorders efficiently and accurately. Thus, we introduce an Artificial Neural Network framework based on CapsNets to categorize vital genetic disorders related to diabetes. Here, a capsule represents a bundle or set of neurons used to retain data about an essential subject and provides precise information in each image. The suggested approach was systematically compared using cutting-edge methods and basic classification models. With an overall 91.4 percent accuracy, the proposed CapsNets-based method provides the best sensitivity89.93%, specificity 90.77%, and F1-score value 93.10%