Deep Learning-Driven Intelligent System for Early Diabetes Prediction and Risk Assessment

Abstract

Diabetes is one of the fastest-growing chronic diseases worldwide, posing significant health and economic challenges due to its long-term complications. Early identification of individuals at high risk of developing diabetes is essential for timely intervention, personalized treatment, and improved patient outcomes. This study proposes a Deep Learning-Driven Intelligent System for Early Diabetes Prediction and Risk Assessment that utilizes advanced deep learning techniques to accurately classify diabetic and non-diabetic individuals based on clinical and physiological attributes. The proposed framework employs a comprehensive data preprocessing pipeline, including missing value handling, normalization, feature selection, and class balancing to enhance data quality and model performance. A deep neural network (DNN) architecture with multiple hidden layers is designed to capture complex nonlinear relationships among patient characteristics such as glucose level, body mass index (BMI), age, insulin concentration, blood pressure, skin thickness, diabetes pedigree function, and other relevant medical parameters. Experimental results demonstrate that the proposed deep learning framework achieves superior predictive performance compared with conventional machine learning algorithms, enabling reliable early-stage diabetes detection and comprehensive risk assessment. Furthermore, the intelligent system can assist healthcare professionals in clinical decision-making by identifying high-risk individuals before the onset of severe diabetic complications. The proposed approach offers a scalable, cost-effective, and automated solution for preventive healthcare and has the potential to be integrated into smart healthcare platforms and telemedicine systems for continuous diabetes screening and personalized risk management.