Predicting the long-term cognitive trajectories using machine learning approaches: A Chinese nationwide longitudinal database.

OBJECTIVES

This study aimed to explore the long-term cognitive trajectories and its' determinants, and construct prediction models for identifying high-risk populations with unfavorable cognitive trajectories.

METHODS

This study included 3502 older adults aged 65-105 years at their first observations in a 16-year longitudinal cohort study. Cognitive function was measured by the Chinese version Mini Mental State Examination. The heterogeneity of cognitive function was identified through mixed growth model. Machine learning algorithms, namely regularized logistic regression (r-LR), support vector machine (SVM), random forest (RF), and super learner (SL) were used to predict cognitive trajectories. Discrimination and calibration metrics were used for performance evaluation.

RESULTS

Two distinct trajectories were identified according to the changes of MMSE scores: intact cognitive functioning (93.6%), and dementia (6.4%). Older age, female gender, Han ethnicity, having no schooling, rural residents, low-frequency leisure activities, and low baseline BADL score were associated with a rapid decline in cognitive function. r-LR, SVM, and SL performed well in predicting cognitive trajectories (Sensitivity: 0.73, G-mean: 0.65). Age and psychological well-being were key predictors.

CONCLUSION

Two cognitive trajectories were identified among older Chinese, and the identified key factors could be targeted for constructing early risk prediction models.