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Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people

文献类型：期刊论文

作者Wang, Shuojia6; Wang, Weiren6; Li, Xiaowen6; Liu, Yafei6; Wei, Jingming5; Zheng, Jianguang6; Wang, Yan3,4; Ye, Birong6; Zhao, Ruihui6; Huang, Yu6
刊名FRONTIERS IN AGING NEUROSCIENCE
出版日期2022-08-11
卷号14页码:12
ISSN号1663-4365
关键词cognitive impairment machine learning risk factor intervention elderly
DOI10.3389/fnagi.2022.977034
通讯作者Zeng, Yanbing(ybingzeng@163.com)
英文摘要Objectives: This study firstly aimed to explore predicting cognitive impairment at an early stage using a large population-based longitudinal survey of elderly Chinese people. The second aim was to identify reversible factors which may help slow the rate of decline in cognitive function over 3 years in the community.Methods: We included 12,280 elderly people from four waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), followed from 2002 to 2014. The Chinese version of the Mini-Mental State Examination (MMSE) was used to examine cognitive function. Six machine learning algorithms (including a neural network model) and an ensemble method were trained on data split 2/3 for training and 1/3 testing. Parameters were explored in training data using 3-fold cross-validation and models were evaluated in test data. The model performance was measured by area-under-curve (AUC), sensitivity, and specificity. In addition, due to its better interpretability, logistic regression (LR) was used to assess the association of life behavior and its change with cognitive impairment after 3 years.Results: Support vector machine and multi-layer perceptron were found to be the best performing algorithms with AUC of 0.8267 and 0.8256, respectively. Fusing the results of all six single models further improves the AUC to 0.8269. Playing more Mahjong or cards (OR = 0.49,95% CI: 0.38-0.64), doing more garden works (OR = 0.54,95% CI: 0.43-0.68), watching TV or listening to the radio more (OR = 0.67,95% CI: 0.59-0.77) were associated with decreased risk of cognitive impairment after 3 years.Conclusions: Machine learning algorithms especially the SVM, and the ensemble model can be leveraged to identify the elderly at risk of cognitive impairment. Doing more leisure activities, doing more gardening work, and engaging in more activities combined were associated with decreased risk of cognitive impairment.
收录类别SCI
WOS关键词UNITED-STATES ; RISK-FACTORS ; DEMENTIA ; TRAJECTORIES ; VALIDATION ; MORTALITY ; DECLINE ; TRENDS ; LIFE
资助项目National Natural Science Foundation of China ; Research Center for Capital Health Management and Policy ; [71874147] ; [2022JD01]
WOS研究方向Geriatrics & Gerontology ; Neurosciences & Neurology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000844362700001
资助机构National Natural Science Foundation of China ; Research Center for Capital Health Management and Policy
源URL[http://ir.psych.ac.cn/handle/311026/43273]  
专题心理研究所_健康与遗传心理学研究室
通讯作者Zeng, Yanbing
作者单位1.Capital Med Univ, Sch Publ Hlth, Beijing, Peoples R China
2.Tencent Healthcare, Shenzhen, Peoples R China
3.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China
5.Peking Univ, Inst Mental Hlth, Beijing, Peoples R China
6.Tencent Jarvis Lab, Shenzhen, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Shuojia,Wang, Weiren,Li, Xiaowen,et al. Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people[J]. FRONTIERS IN AGING NEUROSCIENCE,2022,14:12.
APA Wang, Shuojia.,Wang, Weiren.,Li, Xiaowen.,Liu, Yafei.,Wei, Jingming.,...&Zeng, Yanbing.(2022).Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people.FRONTIERS IN AGING NEUROSCIENCE,14,12.
MLA Wang, Shuojia,et al."Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people".FRONTIERS IN AGING NEUROSCIENCE 14(2022):12.

入库方式： OAI收割

来源：心理研究所

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