Customized Mechanism for Diabetic Risk Prediction: A Hybrid CNN–Autoencoder Approach with Emphasis on Retinal Imaging in the Elderly

Abstract

Diabetes Mellitus presents a substantial health obstacle on a global scale, with a particular impact on the elderly demographic. Prompt identification is vital for efficient control and avoidance of complications. This study introduces a new Hybrid Convolutional Neural Network (CNN) and Autoencoder model specifically developed for accurately predicting the risk of diabetes at an early stage. The model is specifically designed to analyze retinal images in older individuals. The introduction of this paper presents a comprehensive analysis of the increasing incidence of diabetes in the elderly population and underscores the significance of early identification. Conventional approaches frequently encounter constraints in terms of precision and specificity, which has led to the investigation of sophisticated machine learning models. The Hybrid CNN–Autoencoder model combines the advantageous characteristics of both architectures, utilizing the CNN proficiency in extracting spatial features and the Autoencoder's capability for unsupervised feature learning. The approach we use consists of training and validating the model using a comprehensive dataset of retinal images from elderly individuals. The model attains a remarkable accuracy of 90.92%, outperforming the typical deep learning and machine learning models frequently employed in predicting diabetic risk. The experimental results demonstrate the superior performance of the Hybrid CNN–Autoencoder model in terms of accuracy, sensitivity, and specificity. Comparative analysis shows that it is highly effective in identifying subtle patterns that indicate early signs of diabetes, surpassing traditional models and other modern deep learning methods. The research findings presented in this study make a valuable contribution to the expanding knowledge base on diabetes detection, specifically within the elderly population. The proven precision of the suggested model highlights its capacity as a dependable and tailored instrument for early forecasting, thus enabling prompt interventions and individualized healthcare strategies for individuals susceptible to diabetes.