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

Abstract

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.

Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment

“…The classification performance of SVM is greatly influenced by the selection of its own parameters. In many studies, animal swarm algorithms are introduced into the classifiers for optimization [ 37 , 38 ]. The optimized SVM with SSA was adopted to enhance classification performance by optimizing the C and Gamma parameters of the SVM.…”

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

“…The brain topological structure is disrupted after the onset of cognitive impairment, leading to decreased transmission efficiency of individual nodes [ 25 ]. Thus, we select nodal efficiency as the first modal feature.…”

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

“…For instance, Zhang et al 9 classified MCI using improved feature extraction methods for fMRI data. Sheng et al 10 predicted MCI patients' cognitive scores by combining fMRI features. Overall, fMRI provides great convenience for the early diagnosis of neurological diseases 11 …”