Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale
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Published:2024
Issue:3
Volume:21
Page:3838-3859
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Lu Yu1, Liu Tongqiang2, Sheng Quan1, Zhang Yutao1, Shi Haifeng3, Jiao Zhuqing14
Affiliation:
1. School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China 2. Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China 3. Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China 4. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
Abstract
<abstract>
<p>Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Reference45 articles.
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