AI-Based Diabetes Risk Stratification Using Multi-Source Healthcare Data

Abstract

The increasing global prevalence of diabetes has created an urgent need for accurate and early risk stratification methods. Artificial intelligence (AI) has emerged as a powerful tool for predictive healthcare, particularly through the integration of multi-source healthcare data. This study presents a systematic review of 52 peer-reviewed articles published between 2015 and 2024, focusing on AI-based diabetes risk stratification models that utilize diverse data sources, including electronic health records (EHRs), wearable device data, demographic factors, and genomic information. The findings indicate that machine learning and deep learning models significantly enhance predictive performance. Across the reviewed studies, predictive accuracy ranged from 78% to 94%, while sensitivity and specificity averaged 85% and 83%, respectively. Models integrating multi-source data outperformed single-source approaches by 12–18% in accuracy and demonstrated improved generalizability. Additionally, studies incorporating real-time wearable data reported a 15–20% increase in early risk detection rates, enabling more proactive interventions. Ensemble learning techniques achieved the highest performance, with an average area under the curve (AUC) of 0.89–0.93, compared to 0.75–0.82 for traditional statistical models. However, several challenges persist. Only 27% of the studies included external clinical validation, and approximately 35% reported issues related to data heterogeneity and missing values. Concerns regarding model interpretability and patient data privacy were also highlighted in over 40% of the reviewed papers. Overall, the study demonstrates that AI-driven multi-source data integration substantially improves diabetes risk prediction and emphasizes the need for standardized, explainable, and clinically validated models to support real-world implementation.

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