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

Abstract

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.

Constructing brain functional networks with adaptive manifold regularization for early mild cognitive impairment

“…Inspired by Zhang et al, 44 we analyzed the graph theoretical topological properties of the BFN in eMCI patients and normal subjects. The Gretna toolbox was utilized to obtain the AUC for clustering coefficient (Cp), global efficiency (Eg), and local efficiency (Loc) within a threshold range.…”

Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment

“…In view of this, we propose a multimodal classification framework based on hypergraph latent relation (HLR) for classifying ESRDaMCI and normal subjects. Firstly, a brain functional network is constructed based on the method of hypergraph manifold, and the feature matrix of fMRI is extracted by graph theory (GT) [ 19 ]. Secondly, CBF from ASL is selected as the feature matrix for the second modality.…”