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Background This study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty. Methods This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, support vector machine, random forest, artificial neural network, and extreme gradient boosting to predict the risk of long-term frailty trajectories. Further, Shapley additive explanations was employed to identify feature importance and open-up the black box model of machine learning to further strengthen decision makers’ trust in the model. Results Two distinct frailty trajectories (stable-growth: 82.54%, rapid-growth: 17.46%) were identified. Compared with other algorithms, random forest performed relatively better in distinguishing the stable-growth and rapid-growth groups. Physical function including activities of daily living and instrumental activities of daily living, marital status, weight, and cognitive function were top five predictors. Conclusions Interpretable machine learning can achieve the primary goal of risk stratification and make it more transparent in individual prediction beneficial to primary screening and tailored prevention.

Objectives To explore the heterogeneous disability trajectories and construct explainable machine learning models for effective prediction of long-term disability trajectories and understanding the mechanisms of predictions among the elderly Chinese at community level. Methods This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018. A total of 4149 subjects aged 65 + in 2002 with completed activities of daily living (ADL) information for at least three waves were included. The mixed growth model was used to identify disability trajectories, and five machine learning models were further established to predict disability trajectories using epidemiological variables. An explainable approach was deployed to understand the model’s decisions. Results Three distinct disability trajectories, including normal class (77.3%), progressive class (15.5%), and high-onset class (7.2%), were identified for three-class prediction. The latter two were further merged into abnormal class, accompanied by normal class for two-class prediction. Machine learning, especially random forest and extreme gradient boosting achieved good performance in both two tasks. ADL, age, leisure activity, cognitive function, and blood pressure were key predictors. Conclusion The findings suggest that machine learning showed good performance and maybe of additional value in analyzing quality indicators in predicting disability trajectories, thereby providing basis to personalize intervention measures.

Background: Cognitive decline shows heterogeneous with increasing age. The trajectory of cognitive function is yet to be fully investigated in older Chinese. Early identification of such trend is essential for the prevention of high-risk population related to cognitive decline. This study aimed to explore the heterogeneity and determinants of cognitive trajectories, and construct prediction models for distinguishing cognitive trajectories among the elderly Chinese at a community level.Methods: This study included 3502 older adults aged 65-105 years at their first observations in the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018. The Chinese version Mini-Mental State Examination (MMSE) was used for measuring cognitive function. The heterogeneity of cognitive trajectories was identified through mixed growth model, and the determinants of cognitive trajectories were analyzed by logistic regression. Under a consideration of feature selection, machine learning algorithms, namely logistic regression (LR), support vector machine (SVM), and an integrated algorithm combining LR and SVM (stacking), 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: Two distinct cognitive trajectories were identified according to the changes of MMSE scores: stable-function (93.6%), and rapid-decline (6.4%). Older age, female gender, Han ethnicity, having no schooling, rural residents, low frequency leisure activities, and low baseline ADL score were associated with the rapid decline in cognitive function. For classification of two trajectories, the performance of LR, SVM and stacking algorithms with feature selection were comparable, of which achieved 0.66 of balanced accuracy, 0.74 of sensitivity, 0.73 of F1 score, 0.66 of AUROC, and 0.25 of brier score. Among the predictors, age and psychological well-being were the most effective variables for distinguishing two trajectories.Conclusions: Two cognitive trajectories were identified among older Chinese. The identified determinants of trajectories could be targeted for constructing early risk prediction models.

Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.