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Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease

Rao, Patike Kiran; Chatterjee, Subarna; Nagaraju, K; Khan, Surbhi B.; Almusharraf, Ahlam; Alharbi, Abdullah I.

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Authors

Patike Kiran Rao

Subarna Chatterjee

K Nagaraju

Ahlam Almusharraf

Abdullah I. Alharbi



Abstract

Chronic Kidney Disease (CKD) represents a considerable global health challenge, emphasizing the need for precise and prompt prediction of disease progression to enable early intervention and enhance patient outcomes. As per this study, we introduce an innovative fusion deep learning model that combines a Graph Neural Network (GNN) and a tabular data model for predicting CKD progression by capitalizing on the strengths of both graph-structured and tabular data representations. The GNN model processes graph-structured data, uncovering intricate relationships between patients and their medical conditions, while the tabular data model adeptly manages patient-specific features within a conventional data format. An extensive comparison of the fusion model, GNN model, tabular data model, and a baseline model was conducted utilizing various evaluation metrics, encompassing accuracy, precision, recall, and F1-score. The fusion model exhibited outstanding performance across all metrics, underlining its augmented capacity for predicting CKD progression. The GNN model’s performance closely trailed the fusion model, accentuating the advantages of integrating graph-structured data into the prediction process. Hyperparameter optimization was performed using grid search, ensuring a fair comparison among the models. The fusion model displayed consistent performance across diverse data splits, demonstrating its adaptability to dataset variations and resilience against noise and outliers. In conclusion, the proposed fusion deep learning model, which amalgamates the capabilities of both the GNN model and the tabular data model, substantially surpasses the individual models and the baseline model in predicting CKD progression. This pioneering approach provides a more precise and dependable method for early detection and management of CKD, highlighting its potential to advance the domain of precision medicine and elevate patient care.

Citation

Rao, P. K., Chatterjee, S., Nagaraju, K., Khan, S. B., Almusharraf, A., & Alharbi, A. I. (in press). Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease. Diagnostics, 13(12), 1981. https://doi.org/10.3390/diagnostics13121981

Journal Article Type Article
Acceptance Date Jun 1, 2023
Online Publication Date Jun 6, 2023
Deposit Date Jul 6, 2023
Publicly Available Date Jul 6, 2023
Journal Diagnostics
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 13
Issue 12
Pages 1981
DOI https://doi.org/10.3390/diagnostics13121981
Keywords Clinical Biochemistry

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