Attention-Enhanced Multi-View Graph Convolutional Network for Early Prediction of Chronic Kidney Disease

The prediction of chronic kidney disease (CKD) must have models capable of processing heterogeneous clinical data and being transparent to assist clinical decision making. Current CKD research usually uses single-view data, integrated graph representations, or bivalent deep learning systems that do not reflect view-specific clinical connections or cannot be interpreted effectively. The first study that uses a combination of the individual multi-view similarity graphs and an attention-based fusion approach to predict the risk of CKD, and the study overcomes the shortcomings of the earlier machine learning, deep learning, and graph-based models. The suggested Attentive Multi-View Graph Convolutional Network (MV-GCN-Attn) uses Graph Convolutional Networks to learn view-specific embeddings and applies them in an adaptive way with the help of attention mechanisms and highlighted clinically influential features. The model has an accuracy of 91.0% along with a precision of 89.0% and a recall of 92.0% and F1-score of 90.0 in the experiment of 400 patient records and 24 attributes in a publicly available dataset of UCI CKD, which is higher than the conventional baselines. The framework also offers feature- and view-level interpretability and the key indicators are determined: serum creatinine and haemoglobin. These results indicate that the use of multi-view graph learning with attention-based interpretability has the potential to provide effective, clinically significant predictions, which can be used with a high degree of confidence in the practical implementation of CKD screening and decision-support in the work of various healthcare facilities and as a valuable aid in the early clinical intervention process.

@article{Suvaris2025,
  title     = {Attention-Enhanced Multi-View Graph Convolutional Network for Early Prediction of Chronic Kidney Disease},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Roshan D Suvaris and K Nagaiah and P. Satish and Hussana Johar R B and Elangovan Muniyandy and Manasa Adusumilli and Khaled Bedair},
  doi       = {10.14569/IJACSA.2025.0161163},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161163}
}