Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people - Wang, Shuojia; Wang, Weiren; Li, Xiaowen; Liu, Yafei; Wei, Jingming; Zheng, Jianguang; Wang, Yan; Ye, Birong; Zhao, Ruihui; Huang, Yu; Peng, Sixiang; Zheng, Yefeng; et al. (Frontiers in Aging Neuroscience, 2022)
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 4 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 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 Risk-predictive algorithms may serve as a valuable tool to support assessing the risk of cognitive impairment in community-based preventive health care. Modifiable risk factors were identified and suggested to slow the rate of cognitive decline.