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Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality

Chronic kidney disease (CKD) patients have high risks of end-stage kidney disease (ESKD) and pre-ESKD death. Therefore, accurately predicting these outcomes is useful among CKD patients, especially in those who are at high risk. Thus, we evaluated whether a machine-learning system can predict accura...

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Autores principales: Kanda, Eiichiro, Epureanu, Bogdan Iuliu, Adachi, Taiji, Kashihara, Naoki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931312/
https://www.ncbi.nlm.nih.gov/pubmed/36812636
http://dx.doi.org/10.1371/journal.pdig.0000188
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author Kanda, Eiichiro
Epureanu, Bogdan Iuliu
Adachi, Taiji
Kashihara, Naoki
author_facet Kanda, Eiichiro
Epureanu, Bogdan Iuliu
Adachi, Taiji
Kashihara, Naoki
author_sort Kanda, Eiichiro
collection PubMed
description Chronic kidney disease (CKD) patients have high risks of end-stage kidney disease (ESKD) and pre-ESKD death. Therefore, accurately predicting these outcomes is useful among CKD patients, especially in those who are at high risk. Thus, we evaluated whether a machine-learning system can predict accurately these risks in CKD patients and attempted its application by developing a Web-based risk-prediction system. We developed 16 risk-prediction machine-learning models using Random Forest (RF), Gradient Boosting Decision Tree, and eXtreme Gradient Boosting with 22 variables or selected variables for the prediction of the primary outcome (ESKD or death) on the basis of repeatedly measured data of CKD patients (n = 3,714; repeatedly measured data, n = 66,981) in their electronic-medical records. The performances of the models were evaluated using data from a cohort study of CKD patients carried out over 3 years (n = 26,906). One RF model with 22 variables and another RF model with 8 variables of time-series data showed high accuracies of the prediction of the outcomes and were selected for use in a risk-prediction system. In the validation, the 22- and 8-variable RF models showed high C-statistics for the prediction of the outcomes: 0.932 (95% CI 0.916, 0.948) and 0.93 (0.915, 0.945), respectively. Cox proportional hazards models using splines showed a highly significant relationship between the high probability and high risk of an outcome (p<0.0001). Moreover, the risks of patients with high probabilities were higher than those with low probabilities: 22-variable model, hazard ratio of 104.9 (95% CI 70.81, 155.3); 8-variable model, 90.9 (95% CI 62.29, 132.7). Then, a Web-based risk-prediction system was actually developed for the implementation of the models in clinical practice. This study showed that a machine-learning-based Web system is a useful tool for the risk prediction and treatment of CKD patients.
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spelling pubmed-99313122023-02-16 Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality Kanda, Eiichiro Epureanu, Bogdan Iuliu Adachi, Taiji Kashihara, Naoki PLOS Digit Health Research Article Chronic kidney disease (CKD) patients have high risks of end-stage kidney disease (ESKD) and pre-ESKD death. Therefore, accurately predicting these outcomes is useful among CKD patients, especially in those who are at high risk. Thus, we evaluated whether a machine-learning system can predict accurately these risks in CKD patients and attempted its application by developing a Web-based risk-prediction system. We developed 16 risk-prediction machine-learning models using Random Forest (RF), Gradient Boosting Decision Tree, and eXtreme Gradient Boosting with 22 variables or selected variables for the prediction of the primary outcome (ESKD or death) on the basis of repeatedly measured data of CKD patients (n = 3,714; repeatedly measured data, n = 66,981) in their electronic-medical records. The performances of the models were evaluated using data from a cohort study of CKD patients carried out over 3 years (n = 26,906). One RF model with 22 variables and another RF model with 8 variables of time-series data showed high accuracies of the prediction of the outcomes and were selected for use in a risk-prediction system. In the validation, the 22- and 8-variable RF models showed high C-statistics for the prediction of the outcomes: 0.932 (95% CI 0.916, 0.948) and 0.93 (0.915, 0.945), respectively. Cox proportional hazards models using splines showed a highly significant relationship between the high probability and high risk of an outcome (p<0.0001). Moreover, the risks of patients with high probabilities were higher than those with low probabilities: 22-variable model, hazard ratio of 104.9 (95% CI 70.81, 155.3); 8-variable model, 90.9 (95% CI 62.29, 132.7). Then, a Web-based risk-prediction system was actually developed for the implementation of the models in clinical practice. This study showed that a machine-learning-based Web system is a useful tool for the risk prediction and treatment of CKD patients. Public Library of Science 2023-01-18 /pmc/articles/PMC9931312/ /pubmed/36812636 http://dx.doi.org/10.1371/journal.pdig.0000188 Text en © 2023 Kanda et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kanda, Eiichiro
Epureanu, Bogdan Iuliu
Adachi, Taiji
Kashihara, Naoki
Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality
title Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality
title_full Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality
title_fullStr Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality
title_full_unstemmed Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality
title_short Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality
title_sort machine-learning-based web system for the prediction of chronic kidney disease progression and mortality
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931312/
https://www.ncbi.nlm.nih.gov/pubmed/36812636
http://dx.doi.org/10.1371/journal.pdig.0000188
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