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Article

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

1
Department of Computer Science and Engineering, Faculty of Engineering, MS Ramaiah University of Applied Sciences, Bengaluru 560054, Karnataka, India
2
Department of Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing, Kurnool 518008, Andhra Pradesh, India
3
Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK
4
Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
5
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 21911, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Diagnostics 2023, 13(12), 1981; https://doi.org/10.3390/diagnostics13121981
Submission received: 2 April 2023 / Revised: 29 May 2023 / Accepted: 1 June 2023 / Published: 6 June 2023
(This article belongs to the Special Issue Deep Learning Models for Medical Imaging Processing)

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.
Keywords: chronic kidney disease; graph neural network model; GNN; tabular data model; deep learning; prediction; healthcare; CKD chronic kidney disease; graph neural network model; GNN; tabular data model; deep learning; prediction; healthcare; CKD

Share and Cite

MDPI and ACS Style

Rao, P.K.; Chatterjee, S.; Nagaraju, K.; Khan, S.B.; Almusharraf, A.; Alharbi, A.I. Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease. Diagnostics 2023, 13, 1981. https://doi.org/10.3390/diagnostics13121981

AMA Style

Rao PK, Chatterjee S, Nagaraju K, Khan SB, Almusharraf A, Alharbi AI. Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease. Diagnostics. 2023; 13(12):1981. https://doi.org/10.3390/diagnostics13121981

Chicago/Turabian Style

Rao, Patike Kiran, Subarna Chatterjee, K Nagaraju, Surbhi B. Khan, Ahlam Almusharraf, and Abdullah I. Alharbi. 2023. "Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease" Diagnostics 13, no. 12: 1981. https://doi.org/10.3390/diagnostics13121981

APA Style

Rao, P. K., Chatterjee, S., Nagaraju, K., Khan, S. B., Almusharraf, A., & Alharbi, A. I. (2023). Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease. Diagnostics, 13(12), 1981. https://doi.org/10.3390/diagnostics13121981

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