GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease

The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yutao Zhang [verfasserIn] Zhengtao Xi [verfasserIn] Jiahui Zheng [verfasserIn] Haifeng Shi [verfasserIn] Zhuqing Jiao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	end-stage renal disease cognitive function scores model functional magnetic resonance imaging predict

Übergeordnetes Werk:	In: Frontiers in Aging Neuroscience - Frontiers Media S.A., 2010, 14(2022)
Übergeordnetes Werk:	volume:14 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnagi.2022.834331

Katalog-ID:	DOAJ010810323

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520			\|a The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD.
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allfields	10.3389/fnagi.2022.834331 doi (DE-627)DOAJ010810323 (DE-599)DOAJa2e6564fbb404a70b46d2dac676dc377 DE-627 ger DE-627 rakwb eng RC321-571 Yutao Zhang verfasserin aut GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD. end-stage renal disease cognitive function scores model functional magnetic resonance imaging predict Neurosciences. Biological psychiatry. Neuropsychiatry Zhengtao Xi verfasserin aut Jiahui Zheng verfasserin aut Haifeng Shi verfasserin aut Zhuqing Jiao verfasserin aut Zhuqing Jiao verfasserin aut In Frontiers in Aging Neuroscience Frontiers Media S.A., 2010 14(2022) (DE-627)629834350 (DE-600)2558898-9 16634365 nnns volume:14 year:2022 https://doi.org/10.3389/fnagi.2022.834331 kostenfrei https://doaj.org/article/a2e6564fbb404a70b46d2dac676dc377 kostenfrei https://www.frontiersin.org/articles/10.3389/fnagi.2022.834331/full kostenfrei https://doaj.org/toc/1663-4365 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022
spelling	10.3389/fnagi.2022.834331 doi (DE-627)DOAJ010810323 (DE-599)DOAJa2e6564fbb404a70b46d2dac676dc377 DE-627 ger DE-627 rakwb eng RC321-571 Yutao Zhang verfasserin aut GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD. end-stage renal disease cognitive function scores model functional magnetic resonance imaging predict Neurosciences. Biological psychiatry. Neuropsychiatry Zhengtao Xi verfasserin aut Jiahui Zheng verfasserin aut Haifeng Shi verfasserin aut Zhuqing Jiao verfasserin aut Zhuqing Jiao verfasserin aut In Frontiers in Aging Neuroscience Frontiers Media S.A., 2010 14(2022) (DE-627)629834350 (DE-600)2558898-9 16634365 nnns volume:14 year:2022 https://doi.org/10.3389/fnagi.2022.834331 kostenfrei https://doaj.org/article/a2e6564fbb404a70b46d2dac676dc377 kostenfrei https://www.frontiersin.org/articles/10.3389/fnagi.2022.834331/full kostenfrei https://doaj.org/toc/1663-4365 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022
allfields_unstemmed	10.3389/fnagi.2022.834331 doi (DE-627)DOAJ010810323 (DE-599)DOAJa2e6564fbb404a70b46d2dac676dc377 DE-627 ger DE-627 rakwb eng RC321-571 Yutao Zhang verfasserin aut GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD. end-stage renal disease cognitive function scores model functional magnetic resonance imaging predict Neurosciences. Biological psychiatry. Neuropsychiatry Zhengtao Xi verfasserin aut Jiahui Zheng verfasserin aut Haifeng Shi verfasserin aut Zhuqing Jiao verfasserin aut Zhuqing Jiao verfasserin aut In Frontiers in Aging Neuroscience Frontiers Media S.A., 2010 14(2022) (DE-627)629834350 (DE-600)2558898-9 16634365 nnns volume:14 year:2022 https://doi.org/10.3389/fnagi.2022.834331 kostenfrei https://doaj.org/article/a2e6564fbb404a70b46d2dac676dc377 kostenfrei https://www.frontiersin.org/articles/10.3389/fnagi.2022.834331/full kostenfrei https://doaj.org/toc/1663-4365 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022
allfieldsGer	10.3389/fnagi.2022.834331 doi (DE-627)DOAJ010810323 (DE-599)DOAJa2e6564fbb404a70b46d2dac676dc377 DE-627 ger DE-627 rakwb eng RC321-571 Yutao Zhang verfasserin aut GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD. end-stage renal disease cognitive function scores model functional magnetic resonance imaging predict Neurosciences. Biological psychiatry. Neuropsychiatry Zhengtao Xi verfasserin aut Jiahui Zheng verfasserin aut Haifeng Shi verfasserin aut Zhuqing Jiao verfasserin aut Zhuqing Jiao verfasserin aut In Frontiers in Aging Neuroscience Frontiers Media S.A., 2010 14(2022) (DE-627)629834350 (DE-600)2558898-9 16634365 nnns volume:14 year:2022 https://doi.org/10.3389/fnagi.2022.834331 kostenfrei https://doaj.org/article/a2e6564fbb404a70b46d2dac676dc377 kostenfrei https://www.frontiersin.org/articles/10.3389/fnagi.2022.834331/full kostenfrei https://doaj.org/toc/1663-4365 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022
allfieldsSound	10.3389/fnagi.2022.834331 doi (DE-627)DOAJ010810323 (DE-599)DOAJa2e6564fbb404a70b46d2dac676dc377 DE-627 ger DE-627 rakwb eng RC321-571 Yutao Zhang verfasserin aut GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD. end-stage renal disease cognitive function scores model functional magnetic resonance imaging predict Neurosciences. Biological psychiatry. Neuropsychiatry Zhengtao Xi verfasserin aut Jiahui Zheng verfasserin aut Haifeng Shi verfasserin aut Zhuqing Jiao verfasserin aut Zhuqing Jiao verfasserin aut In Frontiers in Aging Neuroscience Frontiers Media S.A., 2010 14(2022) (DE-627)629834350 (DE-600)2558898-9 16634365 nnns volume:14 year:2022 https://doi.org/10.3389/fnagi.2022.834331 kostenfrei https://doaj.org/article/a2e6564fbb404a70b46d2dac676dc377 kostenfrei https://www.frontiersin.org/articles/10.3389/fnagi.2022.834331/full kostenfrei https://doaj.org/toc/1663-4365 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022
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authorswithroles_txt_mv	Yutao Zhang @@aut@@ Zhengtao Xi @@aut@@ Jiahui Zheng @@aut@@ Haifeng Shi @@aut@@ Zhuqing Jiao @@aut@@
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callnumber-first	R - Medicine
author	Yutao Zhang
spellingShingle	Yutao Zhang misc RC321-571 misc end-stage renal disease misc cognitive function scores misc model misc functional magnetic resonance imaging misc predict misc Neurosciences. Biological psychiatry. Neuropsychiatry GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
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topic_title	RC321-571 GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease end-stage renal disease cognitive function scores model functional magnetic resonance imaging predict
topic	misc RC321-571 misc end-stage renal disease misc cognitive function scores misc model misc functional magnetic resonance imaging misc predict misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc end-stage renal disease misc cognitive function scores misc model misc functional magnetic resonance imaging misc predict misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_browse	misc RC321-571 misc end-stage renal disease misc cognitive function scores misc model misc functional magnetic resonance imaging misc predict misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
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title_full	GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
author_sort	Yutao Zhang
journal	Frontiers in Aging Neuroscience
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title_sort	gwls: a novel model for predicting cognitive function scores in patients with end-stage renal disease
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title_auth	GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
abstract	The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD.
abstractGer	The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD.
abstract_unstemmed	The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD.
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title_short	GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
url	https://doi.org/10.3389/fnagi.2022.834331 https://doaj.org/article/a2e6564fbb404a70b46d2dac676dc377 https://www.frontiersin.org/articles/10.3389/fnagi.2022.834331/full https://doaj.org/toc/1663-4365
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up_date	2024-07-03T16:53:15.438Z
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