Progression-Aware Temporal Graph Transformer for Reliable Chronic Kidney Disease Trajectory Prediction

Chronic Kidney Disease (CKD) is a progressive and irreversible condition that requires early prediction of renal deterioration for effective clinical intervention. Existing studies based on static machine learning and conventional deep learning models fail to capture temporal dependencies, evolving biomarker interactions, and longitudinal disease progression patterns, leading to limited predictive reliability in real-world clinical settings. To address these limitations, this study proposes a Progression-Aware Temporal Graph Transformer for reliable CKD trajectory prediction using the Chronic Renal Insufficiency Cohort (CRIC) dataset comprising 5,625 patients with longitudinal clinical follow-ups and repeated biomarker measurements including eGFR, creatinine, albumin, blood pressure, glucose, and HbA1c. The proposed framework integrates a temporal transformer encoder for sequential patient representation, a dynamic biomarker graph to model evolving renal interactions, and a multi-scale temporal attention mechanism to capture both short- and long-term deterioration patterns. The model is implemented using Python with PyTorch and executed on GPU-based computational infrastructure for efficient training and inference. Experimental results demonstrate that the proposed model achieves 95.0% accuracy, 94.2% precision, 95.1% recall, and 94.6% F1-score, improving performance by approximately 2–4% over state-of-the-art baselines such as Random Forest, KNN, GNN, and 1D CNN models. Additionally, survival analysis yields a Concordance Index of 0.952, confirming strong risk-ranking capability for dialysis onset prediction. The framework also maintains robust performance under noisy and missing data conditions, demonstrating strong generalization ability. In conclusion, the proposed model provides an interpretable, scalable, and clinically reliable solution for CKD progression forecasting, enabling early intervention, personalized treatment planning, and improved renal outcome prediction in clinical decision-support systems.

@article{Suvaris2026,
  title     = {Progression-Aware Temporal Graph Transformer for Reliable Chronic Kidney Disease Trajectory Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Roshan D. Suvaris and Padmavathy E and Dilfuza Akabirkhodjaeva and T. K. Rama Krishna Rao and R. Sindhu and Farrukh Sobia and Elangovan Muniyandy and Aseel Smerat},
  doi       = {10.14569/IJACSA.2026.0170662},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170662}
}