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Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients

The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions i...

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Autores principales: Zhang, Yutao, Sheng, Quan, Fu, Xidong, Shi, Haifeng, Jiao, Zhuqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381242/
https://www.ncbi.nlm.nih.gov/pubmed/35983157
http://dx.doi.org/10.1155/2022/8124053
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author Zhang, Yutao
Sheng, Quan
Fu, Xidong
Shi, Haifeng
Jiao, Zhuqing
author_facet Zhang, Yutao
Sheng, Quan
Fu, Xidong
Shi, Haifeng
Jiao, Zhuqing
author_sort Zhang, Yutao
collection PubMed
description The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients.
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spelling pubmed-93812422022-08-17 Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients Zhang, Yutao Sheng, Quan Fu, Xidong Shi, Haifeng Jiao, Zhuqing Comput Intell Neurosci Research Article The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients. Hindawi 2022-08-09 /pmc/articles/PMC9381242/ /pubmed/35983157 http://dx.doi.org/10.1155/2022/8124053 Text en Copyright © 2022 Yutao Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Yutao
Sheng, Quan
Fu, Xidong
Shi, Haifeng
Jiao, Zhuqing
Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients
title Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients
title_full Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients
title_fullStr Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients
title_full_unstemmed Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients
title_short Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients
title_sort integrated prediction framework for clinical scores of cognitive functions in esrd patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381242/
https://www.ncbi.nlm.nih.gov/pubmed/35983157
http://dx.doi.org/10.1155/2022/8124053
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