GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease - Zhang, Yutao; Xi, Zhengtao; Zheng, Jiahui; Shi, Haifeng; Jiao, Zhuqing (Frontiers in Aging Neuroscience, 2022)
The cognitive function scores of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect clinical diagnosis results. 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 ESRD patients. The model incorporated three parts, 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 brain functional networks. 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 ESRD patients. Besides, WOA was introduced to optimize the parameters in LSSVRM kernel function to improve 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 ESRD patients 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.