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

Yucheng Huang; Zishuo Huang; Qingren Yang; Haojie Jin; Tingke Xu; Yating Fu; Yue Zhu; Xiangyang Zhang; Chun Chen

doi:10.3389/fnagi.2023.1283243

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

Front Aging Neurosci. 2023 Oct 23:15:1283243. doi: 10.3389/fnagi.2023.1283243. eCollection 2023.

Authors

Yucheng Huang¹, Zishuo Huang^{1

2}, Qingren Yang^{1

2}, Haojie Jin¹, Tingke Xu¹, Yating Fu¹, Yue Zhu¹, Xiangyang Zhang³, Chun Chen^{1

4}

Affiliations

¹ School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.
² School of Innovation and Entrepreneurship, Wenzhou Medical University, Wenzhou, Zhejiang, China.
³ The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
⁴ Center for Healthy China Research, Wenzhou Medical University, Wenzhou, Zhejiang, China.

Abstract

Background: Mild cognitive impairment (MCI) is a transitory yet reversible stage of dementia. Systematic, scientific and population-wide early screening system for MCI is lacking. This study aimed to construct prediction models using longitudinal data to identify potential MCI patients and explore its critical features among Chinese older adults.

Methods: A total of 2,128 participants were selected from wave 5-8 of Chinese Longitudinal Healthy Longevity Study. Cognitive function was measured using the Chinese version of Mini-Mental State Examination. Long- short-term memory (LSTM) and three machine learning techniques, including 8 sociodemographic features and 12 health behavior and health status features, were used to predict individual risk of MCI in the next year. Performances of prediction models were evaluated through receiver operating curve and decision curve analysis. The importance of predictors in prediction models were explored using Shapley Additive explanation (SHAP) model.

Results: The area under the curve values of three models were around 0.90 and decision curve analysis indicated that the net benefit of XGboost and Random Forest were approximate when threshold is lower than 0.8. SHAP models showed that age, education, respiratory disease, gastrointestinal ulcer and self-rated health are the five most important predictors of MCI.

Conclusion: This screening method of MCI, combining LSTM and machine learning, successfully predicted the risk of MCI using longitudinal datasets, and enables health care providers to implement early intervention to delay the process from MCI to dementia, reducing the incidence and treatment cost of dementia ultimately.

Keywords: China; LSTM (long short-term memory networks); machine learning (ML); mild cognitive impairment; prediction model.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by National Natural Science Foundation of China (72274141), Zhejiang Provincial Natural Science Foundation (LY22G030006), 2023 Joint Project of Science and Technology Department of National Administration of Traditional Chinese Medicine and Zhejiang Administration of Traditional Chinese Medicine (GZY-ZJ-KJ-23084), and Point Leader Research and Development Project (2022C03G1890052).