AI-Driven Predictive Models for Early Detection of Diabetes: A Review Study

Diabetes is a chronic condition with profound global health implications, making early detection critical for effective management and prevention of complications. This paper offers a comprehensive review of AI-driven predictive models for diabetes detection, highlighting their evolution, current trends, and clinical relevance. The review traces the progression from early statistical methods like logistic regression to sophisticated machine learning and deep learning approaches, such as neural networks and gradient boosting machines. These AI techniques have significantly enhanced the accuracy of diabetes risk prediction by leveraging diverse data sources, including electronic health records (EHRs) and continuous glucose monitoring (CGM) devices. The review also explores the integration of multi-omics data, further refining predictive capabilities. Case studies demonstrate the real-world impact of these models, showing substantial improvements in early detection and personaliSed treatment. However, challenges persist, particularly in data security, interdisciplinary collaboration, and ethical AI deployment. This review identifies existing research gaps and proposes future directions, emphasizing the ongoing need for innovation in predictive modelling. The insights provided are intended to inform researchers, clinicians, and policymakers in advancing AI-driven strategies for diabetes prediction and prevention.