Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 15, 2020
Date Accepted: Jan 18, 2021
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Development and validation of risk prediction model for cognitive impairment in Chinese community dwellers with normal cognition: using machine learning approach
ABSTRACT
Background:
Dementia causes huge pressure on families and goverments worldwide. Early detection of individuals at risk of cognitive impairment is critical to reduce the mortality rate.
Objective:
We aimed to build a prediction model based on machine learning for cognitive impairment (CI) in Chinese elderly community dwellers with normal cognition.
Methods:
A prospective cohort of 6,718 older people from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), followed from 2008-2011, was used to develop and validate the prediction model. CI was identified using Chinese version of the Mini-Mental State Examination (CMMSE). Several machine learning algorithms (Random Forest, XGBoost, Naïve Bayes and logistic regression) were used to model 3-year risk of CI. We explored optimal cutoffs and adjusted parameters in validation data and evaluated the model in test data. Nomogram was established to vividly present the prediction model.
Results:
Mean age was 80.4 ± 10.3 years and 50.8% were female. During 3-year follow-up, 991 (14.8%) participants were identified as CI. Four features were finally selected to develop model, including age, IADL, marital status and baseline cognitive function. The Concordance index of the model constructed by logistic regression were 0.814 (95%CI 0.781 - 0.846). Older people with normal cognitive function who had a nomogram score of less than 170 or 170 or greater were considered to have low or high 3-year risks of CI, respectively.
Conclusions:
The simple and feasible CI prediction model could identify Chinese elderly community dwellers at greatest risk for CI. This practical model presented by nomogram could be used to screen Chinese elderly community dwellers for CI and to target intervention strategies.
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