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Early Prediction of Type 2 Diabetes Using Multimodal data and Tabular Transformers

arXiv 2601.12981 ↗First posted 19 Jan 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

This study introduces a novel approach for early Type 2 Diabetes Mellitus (T2DM) risk prediction using a tabular transformer (TabTrans) architecture to analyze longitudinal patient data. By processing patients` longitudinal health records and bone-related tabular data, our model captures complex, long-range dependencies in disease progression that conventional methods often overlook. We validated our TabTrans model on a retrospective Qatar BioBank (QBB) cohort of 1,382 subjects, comprising 725 men (146 diabetic, 579 healthy) and 657 women (133 diabetic, 524 healthy). The study integrated electronic health records (EHR) with dual-energy X-ray absorptiometry (DXA) data. To address class imbalance, we employed SMOTE and SMOTE-ENN resampling techniques. The proposed model`s performance is evaluated against conventional machine learning (ML) and generative AI models, including Claude 3.5 Sonnet (Anthropic`s constitutional AI), GPT-4 (OpenAI`s generative pre-trained transformer), and Gemini Pro (Google`s multimodal language model). Our TabTrans model demonstrated superior predictive performance, achieving ROC AUC ≥ 79.7 % for T2DM prediction compared to both generative AI models and conventional ML approaches. Feature interpretation analysis identified key risk indicators, with visceral adipose tissue (VAT) mass and volume, ward bone mineral density (BMD) and bone mineral content (BMC), T and Z-scores, and L1-L4 scores emerging as the most important predictors associated with diabetes development in Qatari adults. These findings demonstrate the significant potential of TabTrans for analyzing complex tabular healthcare data, providing a powerful tool for proactive T2DM management and personalized clinical interventions in the Qatari population. Index Terms: tabular transformers, multimodal data, DXA data, diabetes, T2DM, feature interpretation, tabular data

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References · 4

  1. 2021Transformers in Vision: A Surveycited by 1,013
  2. 2020TabTransformer: Tabular Data Modeling Using Contextual Embeddingscited by 409
  3. 2021Well-tuned Simple Nets Excel on Tabular Datasetscited by 151
  4. 2025CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Datacited by 3

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  4. 2024Towards Holistic Disease Risk Prediction using Small Language Models
  5. 2026Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
  6. 2023Multimodal LLMs for health grounded in individual-specific data
  7. 2025Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs
  8. 2025Chronic Kidney Disease Prognosis Prediction Using Transformer