Machine learning-based prediction of cognitive trajectories among middle-aged and elderly Chinese adults

Abstract

Abstract Background Cognition represents heterogeneous in trajectories with increasing age. Early identification of such trend is essential for the prevention of high risk population related to cognition decline. This study aimed to explore the heterogeneity and determinants of cognitive trajectories, and construct prediction models for distinguishing cognitive trajectories among the middle-aged and elderly Chinese people at a community level. Methods Data was retrospectively collected from the China Health and Retirement Longitudinal Study with a consecutive survey in 3 waves (2011, 2013, 2015). The Mini-mental State Examination (MMSE) scale was used for cognitive measurement. The heterogeneity of cognitive trajectories was identified through mixed growth model, and the determinants of cognitive trajectories were analyzed by logistic regression. Machine learning (ML) algorithms, namely logistic regression (LR), support vector machine (SVM), and random forest (RF), combined with recursive feature elimination were used to predict cognitive trajectories using epidemiological variables. Area under the receiver operating characteristic curve (AUROC) and brier score were used to assess discrimination and calibration, respectively. Results Three cognitive trajectories were identified: “persistently-high” (PH, 83.3%) class with a high cognitive score in baseline and remained high subsequently, “medium-decrease” (MD, 6.6%) class with a medium cognitive score in baseline and a rapid decline subsequently, “low-increase” (LI, 10.1%) with the lowest baseline cognitive score and a rapid growth subsequently. For classification of PH vs. LI and MD vs. LI, the AUROCs of three ML methods were almost larger than 0.90, and brier scores were almost less than 0.10. For PH vs. MD, ML performed less well with the maximum AUROCs close to 0.68, while the calibration was still good. The initial MMSE score was the most significant predictors for distinguishing MD vs. LI and PH vs. LI, while age was the most important predictors in distinguishing MD vs. PH. Conclusions Prediction of cognitive trajectories and identification of its key predictors are of great significance for understanding the heterogeneity of cognitive trajectories as well as interventions for cognitive decline.