Abstract: 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.

Roshan D Suvaris, K Nagaiah, P. Satish, Hussana Johar R B, Elangovan Muniyandy, Manasa Adusumilli and Khaled Bedair. “Attention-Enhanced Multi-View Graph Convolutional Network for Early Prediction of Chronic Kidney Disease”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161163

@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},
doi = {10.14569/IJACSA.2025.0161163},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161163},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Roshan D Suvaris and K Nagaiah and P. Satish and Hussana Johar R B and Elangovan Muniyandy and Manasa Adusumilli and Khaled Bedair}
}