DRL-CNN Technique for Diabetes Prediction

In this research process, a medical decision model is developed for disease prediction based on DL (Deep Learning) models. The major benefits of computer-based algorithms are exact results, adaptability, transparency, and better decision-making. The proposed work three major steps are preprocessing, feature selection and classification. Firstly preprocessing, data analysis pre-processing is the major step in identifying exact methods. Most of the clinical data consists of missing information and inconsistent data. WB-SMOTE (Weighted Borderline Synthetic minority oversampling technique) concept is applied to asses and solves the unbalanced. Secondly feature selection, selections of features are the process of choosing a subgroup of the most associated attributes in the concerned dataset to indicate the final identifier. Wrapper-based approaches are applied to extract the features from the given dataset. Finally classification, accurate prediction of diabetic disease based selected number of features. Classification approaches are Decision Tree (DT), Random Forest (RF) and Enhanced Convolution Neural Network Layer (ECNN). The output comparison among the DRL-OCNN model and some other ML Models is offered. While analyzing diabetes data, it is identified that DRL-OCNN models produce better results with 95.75% of accuracy rate. The received results demonstrate that this suggested DRL-OCNN model produces better performances with a precision of 0.93 and recall of 0.91. This enhancement can decrease time, labor services, effort, and decision exactness. The planned system was assessed on PID (Pima Indians Diabetes) and illustrates an excellent performance in forecasting diabetes illness. The tool used for execution is python.