An Optimal Framework for Intelligent Prediction of Prediabetes and Type-2 Diabetes Using Genomic Data

Abstract

The most important study in medical research is the prediction of type 2 diabetes in patients. As far as type 2 diabetes prediction models go, there are a number of options. Nevertheless, due to subpar quality, the desired outcome was not achieved. Nan characteristics in the gene data make type 2 diabetes prediction as complicated as possible. Scores for both performance and prediction were low due to the flaws. Consequently, the plan is to create a new method for predicting type 2 diabetes using chimpanzees and functional link neural architecture (CbFLNA). Methods such as feature selection, categorization, and gene expression have been carried out as part of the pre-processing methodology. The first steps were to import the genomic database, preprocess the data, and extract the meaningful features. Next, sort the individuals’ illnesses according to the likelihood that they have type 2 diabetes. When all was said and done, the model’s performance was evaluated, and it achieved a very high accuracy score in the prediction.