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Updated: Jan 13, 2026
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 prediction model for mild cognitive impairment in older Chinese adults with
Lulu Yan1,2, Yuanyuan Peng3, Chenjiao Guo3
1Health Science Center, Yangtze University, Jingzhou City, Hubei Province, 434023, China.
Background:
As the population continues to age, the prevalence of mild cognitive impairment (MCI) has increased steadily. Studies have shown that older adults with chronic diseases are more likely to develop MCI than those without chronic conditions, suggesting that chronic diseases may play a significant role in the onset of MCI. Therefore, this study is designed to develop a predictive model for MCI among older individuals with chronic diseases in China and to identify the major factors influencing the occurrence of MCI.
Method:
The training and internal validation data are from the 2018 China Health and Retirement Longitudinal Study (CHARLS), using retrospective data with 4,712 older adults with chronic diseases. The external validation data are from the General Hospital of Southern Theater Command in Guangdong, using prospective data with 1,000 cases. This is an observational study. Univariate logistic regression was used to select statistically significant predictors, and ultimately, a combination of LASSO regression and random forest results identified 9 optimal predictors, which were used to construct the nomogram. The model's discrimination, calibration, clinical applicability, and generalizability were assessed using the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and internal validation.
Results:
Age, education level, child satisfaction, marital status, depressive symptoms, ADL score, income, SCD, and the number of chronic diseases were identified as significant predictors of MCI in older adults with chronic diseases. The AUC values exceeded 0.7 across the training, internal validation, and external validation sets.The calibration curves closely align with the diagonal, and the P values of the Hosmer-Lemeshow test are all greater than 0.05, indicating strong consistency between the predicted and actual outcomes. The DCA further demonstrates that the model has significant clinical utility.
Conclusion:
The nomogram prediction model deeloped in this study demonstrated good predictive performance and may serve as a useful tool to help identify older adults with chronic diseases who are at increased risk of MCI. These findings may inform future strategies for individualized risk assessment and early management.
Insights
A new predictive model helps identify older adults with chronic diseases at risk for mild cognitive impairment (MCI). Key factors include age, education, and depressive symptoms, aiding early intervention strategies.
Area of Science:
- Gerontology
- Neurology
- Public Health
Background:
- Aging populations face rising rates of mild cognitive impairment (MCI).
- Chronic diseases are linked to increased MCI risk in older adults.
- Identifying MCI predictors in this demographic is crucial for public health.
Purpose of the Study:
- Develop a predictive model for MCI in Chinese older adults with chronic diseases.
- Identify key factors associated with MCI development in this population.
- Validate the model's performance for clinical applicability.
Main Methods:
- Utilized data from the China Health and Retirement Longitudinal Study (CHARLS) and a hospital-based cohort.
- Employed logistic regression, LASSO, and random forest to select 9 optimal predictors.
- Assessed model performance using ROC curves, calibration curves, and decision curve analysis (DCA).
Main Results:
- Identified age, education, child satisfaction, marital status, depressive symptoms, ADL score, income, SCD, and number of chronic diseases as significant predictors.
- Achieved AUC values over 0.7 in training, internal, and external validation sets.
- Demonstrated strong calibration and significant clinical utility via DCA.
Conclusions:
- The developed nomogram model shows good predictive performance for MCI in older adults with chronic diseases.
- This tool can aid in identifying high-risk individuals for targeted interventions.
- Findings support individualized risk assessment and early management strategies for MCI.
