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Updated: Jun 19, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development and validation of a risk scoring tool for predicting incident reversible cognitive frailty among
Qinqin Liu1, Huaxin Si2, Yanyan Li1
1School of Nursing, Peking University, Beijing, China.
Aim:
Reversible cognitive frailty (RCF) is an ideal target to prevent asymptomatic cognitive impairment and dependency. This study aimed to develop and validate prediction models for incident RCF.
Methods:
A total of 1230 older adults aged ≥60 years from China Health and Retirement Longitudinal Study 2011-2013 survey were included as the training set. The modified Poisson regression and three machine learning algorithms including eXtreme Gradient Boosting, support vector machine and random forest were used to develop prediction models. All models were evaluated internally with fivefold cross-validation, and evaluated externally using a temporal validation method through the China Health and Retirement Longitudinal Study 2013-2015 survey.
Results:
The incidence of RCF was 27.4% in the training set and 27.5% in the external validation set. A total of 13 important predictors were selected to develop the model, including age, education, contact with their children, medical insurance, vision impairment, heart diseases, medication types, self-rated health, pain locations, loneliness, self-medication, night-time sleep and having running water. All models showed acceptable or approximately acceptable discrimination (AUC 0.683-0.809) for the training set, but fair discrimination (AUC 0.568-0.666) for the internal and external validation. For calibration, only modified Poisson regression and eXtreme Gradient Boosting were acceptable in the training set. All models had acceptable overall prediction performance and clinical usefulness. Older adults were divided into three groups by the risk scoring tool constructed based on modified Poisson regression: low risk (≤24), median risk (24-29) and high risk (>29).
Conclusions:
This risk tool could assist healthcare providers to predict incident RCF among older adults in the next 2 years, facilitating early identification of a high-risk population of RCF. Geriatr Gerontol Int 2024; 24: 874-882.
