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

Zhang, Yutao; Xi, Zhengtao; Zheng, Jiaojiao; Shi, Haifeng; Jiao, Zhuqing

doi:10.3389/fnagi.2022.834331

Cited by 5 publications

(7 citation statements)

References 47 publications

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“…Firstly, the aLp, aLambda, Eglobal, and corresponding cognitive function scores of 40 subjects in the ESRD group were taken as the dataset D. Then, the whole data set was randomly divided into 40 pieces, 39 of which were used as training set S and 1 as test set T in turn for verification. This article selects mean absolute error (MAE) and mean absolute percentage error (MAPE) as evaluation standard 30 MAE is defined as:

MAE = \frac{1}{n} {false\sum}_{i = 1}^{n} |{\overset{s}{true}}_{i} - s_{i}|

…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Global iterative optimization framework for predicting cognitive function statuses of patients with end‐stage renal disease

Sheng

Zhang

Shi

et al. 2023

Int J Imaging Syst Tech

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The existing methods to determine the cognitive function level of end-stage renal disease (ESRD) are not only inaccurate but also susceptible to the influence of the patient's education level, emotional state, and examination environment. We proposed a global iterative optimization framework (GIO) to accurately predict cognitive function statuses of ESRD patients without being affected by the above factors. First, the functional magnetic resonance imaging (fMRI) data preprocessed and the time series were extracted to construct brain functional networks. Secondly, the areas under curve (AUC) of topological attribute parameters in the brain functional networks were extracted as features. After statistical analysis, the global efficiency, the characteristic path length, and the shortest path length in the small-world network were selected as features for linear fusion. Finally, the support vector regression (SVR) was used as the basis of GIO framework, and the global iterative search strategy was introduced to find out the most appropriate penalty factor and radial basis function parameters. Achieving the objective of accurately predicting the cognitive function status of patients with end-stage renal disease. Since this framework uses SVR which has special advantages in processing small sample data, and introduces appropriate parameter selection method, the prediction ability of GIO framework has been significantly improved. Experimental results demonstrate that the final prediction accuracy of the proposed framework is significantly better than those of SVR, LSSVR, GMO-SVR, GMO-LSSVR, and PSO-SVR. The mean absolute error (MAE) and the mean absolute percentage error (MAPE) of the proposed framework are 0.55% and 2.58%, respectively. It is suggested that this framework is more convenient to determine the current level of cognitive function in ESRD patients and is conducive to the early prevention of cognitive dysfunction in ESRD patients.

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MAE = \frac{1}{n} {false\sum}_{i = 1}^{n} |{\overset{s}{true}}_{i} - s_{i}|

…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This article selects mean absolute error (MAE) and mean absolute percentage error (MAPE) as evaluation standard. 30 1. MAE is defined as:…”

Section: Experiments Settingsmentioning

confidence: 99%

Global iterative optimization framework for predicting cognitive function statuses of patients with end‐stage renal disease

Sheng

Zhang

Shi

et al. 2023

Int J Imaging Syst Tech

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“…Meanwhile, the AUC of Elocal accounted for the highest proportion in the feature set. Obviously, the AUC of Elocal was selected as the feature to construct GPSV [ 30 ], GPLSV [ 31 ], GPWLSV [ 32 ], and GPLWLSV for predicting the clinical scores of cognitive functions in ESRD patients [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

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

Zhang

Sheng

et al. 2022

Computational Intelligence and Neuroscience

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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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“…WOA can optimize the selection strategy of kernel function parameters and further improve the operating efficiency of the model ( Zhang et al, 2018 ). The combination of LSSVRM and WOA takes work efficiency and prediction accuracy into account ( Zhang et al, 2022 ). In our study, the prediction models based on functional networks showed a relatively great prediction accuracy, with the prediction accuracy based on combined global and nodal measures being slightly higher than that based only on a single type of measures.…”

Section: Discussionmentioning

confidence: 99%

“…Further, we tentatively predicted the cognitive function of ESRD patients using topological properties as features based on the individual level. The least squares support vector regression machine (LSSVRM) was used to build a prediction model, and the whale optimization algorithm (WOA) was used to optimize model parameters ( Zhang et al, 2022 ). In conclusion, our study attempted to improve the possibility of early diagnosis and neuroprotective treatments for ESRD patients by using predictive models based on neuroimaging techniques.…”

Section: Introductionmentioning

confidence: 99%

Aberrant brain gray matter and functional networks topology in end stage renal disease patients undergoing maintenance hemodialysis with cognitive impairment

Zheng

Wu²,

Dai³

et al. 2022

Front. Neurosci.

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PurposeTo characterize the topological properties of gray matter (GM) and functional networks in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis to provide insights into the underlying mechanisms of cognitive impairment.Materials and methodsIn total, 45 patients and 37 healthy controls were prospectively enrolled in this study. All subjects completed resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) examinations and a Montreal cognitive assessment scale (MoCA) test. Differences in the properties of GM and functional networks were analyzed, and the relationship between brain properties and MoCA scores was assessed. Cognitive function was predicted based on functional networks by applying the least squares support vector regression machine (LSSVRM) and the whale optimization algorithm (WOA).ResultsWe observed disrupted topological organizations of both functional and GM networks in ESRD patients, as indicated by significantly decreased global measures. Specifically, ESRD patients had impaired nodal efficiency and degree centrality, predominantly within the default mode network, limbic system, frontal lobe, temporal lobe, and occipital lobe. Interestingly, the involved regions were distributed laterally. Furthermore, the MoCA scores significantly correlated with decreased standardized clustering coefficient (γ), standardized characteristic path length (λ), and nodal efficiency of the right insula and the right superior temporal gyrus. Finally, optimized LSSVRM could predict the cognitive scores of ESRD patients with great accuracy.ConclusionDisruption of brain networks may account for the progression of cognitive dysfunction in ESRD patients. Implementation of prediction models based on neuroimaging metrics may provide more objective information to promote early diagnosis and intervention.

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

Cited by 5 publications

References 47 publications

Global iterative optimization framework for predicting cognitive function statuses of patients with end‐stage renal disease

Global iterative optimization framework for predicting cognitive function statuses of patients with end‐stage renal disease

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

Aberrant brain gray matter and functional networks topology in end stage renal disease patients undergoing maintenance hemodialysis with cognitive impairment

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