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Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease
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 combi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297131/ https://www.ncbi.nlm.nih.gov/pubmed/37370876 http://dx.doi.org/10.3390/diagnostics13121981 |
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author | Rao, Patike Kiran Chatterjee, Subarna Nagaraju, K Khan, Surbhi B. Almusharraf, Ahlam Alharbi, Abdullah I. |
author_facet | Rao, Patike Kiran Chatterjee, Subarna Nagaraju, K Khan, Surbhi B. Almusharraf, Ahlam Alharbi, Abdullah I. |
author_sort | Rao, Patike Kiran |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10297131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102971312023-06-28 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. Diagnostics (Basel) Article 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. MDPI 2023-06-06 /pmc/articles/PMC10297131/ /pubmed/37370876 http://dx.doi.org/10.3390/diagnostics13121981 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rao, Patike Kiran Chatterjee, Subarna Nagaraju, K Khan, Surbhi B. Almusharraf, Ahlam Alharbi, Abdullah I. Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease |
title | Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease |
title_full | Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease |
title_fullStr | Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease |
title_full_unstemmed | Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease |
title_short | Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease |
title_sort | fusion of graph and tabular deep learning models for predicting chronic kidney disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297131/ https://www.ncbi.nlm.nih.gov/pubmed/37370876 http://dx.doi.org/10.3390/diagnostics13121981 |
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