Early-Stage Diabetes Prediction using Optinet Deep learning techniques considering Comorbid Health Conditions

Abstract

This research presents an innovative methodology for early diabetes prediction, taking into consideration comorbid health conditions. The approach, named "DiabNetTune," integrates advanced deep learning techniques, including optimized Recurrent Neural Networks (RNNs), alongside Convolutional Neural Networks (CNNs) and traditional Random Forest algorithms. By harnessing RNNs to capture temporal dependencies within clinical data and CNNs to extract spatial features, the model provides a comprehensive analysis of the intricate relationships between clinical variables and diabetes onset. Furthermore, the incorporation of Random Forest enhances predictive accuracy and interpretability, facilitating the identification of key predictive factors. Through rigorous experimentation, the proposed framework demonstrates superior performance compared to Deep learning methods, underscoring the efficacy of optimized RNN hyperparameters, CNNs, and Random Forest in advancing personalized diabetes risk assessment and intervention strategies.