Discovery of type 2 diabetes mellitus with correlation and optimization driven hybrid deep learning approach.

Abstract

Diabetes mellitus is a severe condition that has the potential to impair strength. The disease known as diabetes mellitus, which is a chronic condition, is brought on by a significant rise in blood glucose levels. The diagnosis of this condition is made using a variety of chemical and physical testing. Diabetes, however, can harm the organs if it goes undetected. This study develops a hybrid deep-learning technique to recognize Type 2 diabetes mellitus. The data is cleaned up at the pre-processing stage using a data transformation technique based on the Yeo-Jhonson transformation. The tanimoto similarity is used in the feature selection process to select the best features from the data. To prepare data for future processing, data augmentation is performed. The Deep Residual Network and the Rider-based Neural Network are recommended and trained separately for the T2DM identification using the Competitive Multi-Verse Rider Optimizer. The outputs generated by the RideNN and DRN classifiers are blended using correlation-based fusion. The suggested CMVRO-based NN-DRN has shown improved performance with the highest accuracy of 91.4%, sensitivity of 94.8%, and specificity of 90.1%.