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

Abstract

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.

Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

“…Prediction models were selected based on the following criteria: the model was developed for use in China, was reproducible using the CLHLS, and had an AUC of >0.75 during development. We selected 3 models published in Zhou et al [ 8 ], Hu et al [ 5 ], and Wang et al [ 7 ]. Each model was developed for use in the general Chinese population and showed excellent predictive performance (AUC>0.80) during development.…”

“…From Hu et al [ 5 ], the recreated model included age, marital status, IADL, and baseline MMSE score. Lastly, the model from Wang et al [ 7 ] included age; education; sex; ADL; baseline MMSE; and whether the participant gardens, reads newspapers or books, plays mahjong or cards, watches TV, or listens to the radio.…”

“…Thereafter, each model was used to make predictions on the validation set for the general population and 6 subpopulations: male, female, rural-dwelling, urban-dwelling, educated, and not formally educated older adults. The same procedure was also followed for evaluating the prediction models from Zhou et al [ 8 ], Hu et al [ 5 ], and Wang et al [ 7 ]. Average AUCs and accompanying 95% CIs were calculated across the 200 validation-set AUCs for each model in this study.…”

“…In addition to known risk factors, it is unclear if existing prediction models for future cognitive impairment are equally accurate across different socioeconomic groups in China. Several published models have reported areas under the receiver operating characteristic curve (AUCs) greater than 0.80 in their development cohorts [ 5 7 8 ], but each model has only been tested on the general population. Nearly all existing models make predictions by leveraging measures of cognition, age, and education.…”

Predicting mild cognitive impairment among Chinese older adults: a longitudinal study based on long short-term memory networks and machine learning

“…Chronic diseases, such as hypertension, stroke, and diabetes as well as harmful lifestyle behaviors, such as smoking and alcohol consumption, significantly increase the risk of MCI, while regular physical exercise, tea/coffee consumption, and playing Mahjong can prevent cognitive impairment ( Kivipelto et al, 2018 ; Kakutani et al, 2019 ; Zhang et al, 2020 , 2022 ). Owing to limitations in conventional regression methods in terms of collinearity potentially affecting predictors, some studies have applied machine learning based on imaging data or biomarkers to further determine whether an individual has MCI and to explore key features of MCI ( Mirzaei et al, 2016 ; Wang et al, 2022 ; Alamro et al, 2023 ). However, most machine learning studies have only used single-wave panel data, and neurodegenerative disorders have a natural history of progression, thus ignoring the dynamic and longitudinal nature of these diseases, such that early identification and intervention could be sufficient.…”