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

This study explored the integration of advanced deep learning with key pharmaceutical biomarkers to enhance early diabetes prediction. We developed a multimodal ensemble approach that leverages transformer architectures to capture complex dependencies in heterogeneous healthcare data and Diffusion Models to address class imbalances by generating synthetic samples. Our research utilized diverse data sources, including electronic health records, medical imaging, and wearable device time-series data, supplemented with synthetic samples to better represent minority populations such as patients with type 1 and gestational diabetes. Critical biomarkers, including C-peptide, insulin, and hemoglobin A1c, were incorporated to improve model interpretability. The methodology involved extensive evaluation using accuracy, area under the receiver operating characteristic (ROC) curve (AUC), precision, recall, and F1-score, with cross-validation to mitigate overfitting. We also implemented interpretability features to provide clinicians with insight into the significance of biomarkers. Results showed a 6.2% improvement in minority class recall when pharmaceutical biomarkers were combined with diffusion-based augmentation. The model demonstrated enhanced classification stability and provided clear insights into clinical decision-making, highlighting the influence of biomarkers on disease progression and treatment outcomes. Future work will focus on multicenter validation, integration of additional omics data, and specialized validation across diverse populations. These findings underscore the potential of AI-driven biomarker analysis for advancing early diagnosis and personalized diabetes management, with broader implications for chronic disease prediction.