Integrated biomarker analysis and next-generation AI for precision diabetes prediction.

Area of Science:

• Artificial Intelligence in Healthcare
• Biomarker Discovery
• Diabetes Mellitus Research

Background:

• Early diabetes prediction remains challenging, particularly for type 1 and gestational diabetes.
• Heterogeneous healthcare data (EHRs, imaging, wearables) offer potential but require sophisticated analysis.
• Class imbalance in datasets hinders accurate prediction for minority patient groups.

Purpose of the Study:

• To develop an AI model for enhanced early diabetes prediction using multimodal data and key pharmaceutical biomarkers.
• To address class imbalance issues in diabetes datasets through synthetic data generation.
• To improve model interpretability and provide clinical decision support.

Main Methods:

• A multimodal ensemble deep learning approach utilizing transformer architectures and Diffusion Models.
• Integration of diverse data sources: electronic health records, medical imaging, and wearable time-series data.
• Incorporation of pharmaceutical biomarkers (C-peptide, insulin, HbA1c) and synthetic data augmentation for minority classes.

Main Results:

• A 6.2% improvement in minority class recall was achieved by combining pharmaceutical biomarkers with diffusion-based augmentation.
• The AI model demonstrated enhanced classification stability and interpretability.
• The study highlighted the influence of biomarkers on disease progression and treatment outcomes.

Conclusions:

• AI-driven analysis of pharmaceutical biomarkers shows significant potential for advancing early diabetes diagnosis.
• The multimodal ensemble approach with data augmentation improves prediction accuracy and equity.
• Findings support personalized diabetes management and have broader implications for chronic disease prediction.