Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people | |
Wang, Shuojia1; Wang, Weiren1; Li, Xiaowen1; Liu, Yafei1; Wei, Jingming2; Zheng, Jianguang1; Wang, Yan3,4; Ye, Birong1; Zhao, Ruihui1; Huang, Yu1; Peng, Sixiang5; Zheng, Yefeng1; Zeng, Yanbing6 | |
第一作者 | Shuojia Wang |
通讯作者邮箱 | ybingzeng@163.com (yanbing zeng) |
心理所单位排序 | 3 |
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. |
关键词 | cognitive impairment machine learning risk factor intervention elderly |
2022-08-11 | |
语种 | 英语 |
DOI | 10.3389/fnagi.2022.977034 |
发表期刊 | FRONTIERS IN AGING NEUROSCIENCE |
ISSN | 1663-4365 |
卷号 | 14页码:12 |
期刊论文类型 | 实证研究 |
收录类别 | SCI |
资助项目 | National Natural Science Foundation of China ; Research Center for Capital Health Management and Policy ; [71874147] ; [2022JD01] |
出版者 | FRONTIERS MEDIA SA |
WOS关键词 | UNITED-STATES ; RISK-FACTORS ; DEMENTIA ; TRAJECTORIES ; VALIDATION ; MORTALITY ; DECLINE ; TRENDS ; LIFE |
WOS研究方向 | Geriatrics & Gerontology ; Neurosciences & Neurology |
WOS类目 | Geriatrics & Gerontology ; Neurosciences |
WOS记录号 | WOS:000844362700001 |
资助机构 | National Natural Science Foundation of China ; Research Center for Capital Health Management and Policy |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.psych.ac.cn/handle/311026/43273 |
专题 | 健康与遗传心理学研究室 |
通讯作者 | Zeng, Yanbing |
作者单位 | 1.Tencent Jarvis Lab, Shenzhen, Peoples R China 2.Peking Univ, Inst Mental Hlth, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 5.Tencent Healthcare, Shenzhen, Peoples R China 6.Capital Med Univ, Sch Publ Hlth, Beijing, 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. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Using machine learni(924KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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