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...
| Publicado en: | Frontiers in Aging Neuroscience Vol. 13; pp. 1 - 11 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2/3/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155104486&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155104486 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2/3/2022 vid: 13 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 155104486 155104486 155104486 10.3389/fnagi.2022.834331 155104486 ppf: 1 ppct: 10 formats: tig: atl: GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease. aug: au: Zhang, Yutao Xi, Zhengtao Zheng, Jiahui Shi, Haifeng Jiao, Zhuqing affil: School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China sug: subj: Kidney Failure, Chronic Cognition Algorithms Prediction Models Human Magnetic Resonance Imaging Support Vector Machine Male Female Adult Middle Age Age Factors Educational Status Sex Factors Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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