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

Author:

Sheng Quan1,Zhang Yutao1,Shi Haifeng2,Jiao Zhuqing3ORCID

Affiliation:

1. School of Microelectronics and Control Engineering Changzhou University Changzhou People's Republic of China

2. Department of Radiology The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University Changzhou People's Republic of China

3. School of Computer Science and Artificial Intelligence Changzhou University Changzhou People's Republic of China

Abstract

AbstractThe 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.

Funder

Jiangsu Provincial Key Research and Development Program

National Natural Science Foundation of China

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

Reference44 articles.

1. Progress in MRI study of cognitive dysfunction in patients with end‐stage renal disease;Peng C;Chin J Integr Tradit Western Med Imaging,2021

2. Resting‐state functional MRI for observation on changes of functional connectivity of anterior cingulate cortex and correlations with cognitive function in patients with end‐stage renal disease;Fang J;Chin J Med Imag Technol

3. Kidney–brain crosstalk in the acute and chronic setting

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3