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.

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.