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Updated: Jun 25, 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 prediction model for mild cognitive impairment in elderly patients with type 2
Qian Yu1, Xing Jiang1, Jiarong Yan1
1Postgraduate student, Department of Nursing, Jinzhou Medical University, Jinzhou 121001, Liaoning, China.
Background:
The prevalence of mild cognitive impairment (MCI) is steadily increasing among elderly people with type 2 diabetes (T2DM). This study aimed to create and validate a predictive model based on a nomogram.
Methods:
This cross-sectional study collected sociodemographic characteristics, T2DM-related factors, depression, and levels of social support from 530 older adults with T2DM. We used LASSO regression and multifactorial logistic regression to determine the predictors of the model. The performance of the nomogram was evaluated using calibration curves, receiver operating characteristics (ROC), and decision curve analysis (DCA).
Results:
The nomogram comprised age, smoking, physical activity, social support, depression, living alone, and glycosylated hemoglobin. The AUC for the training and validation sets were 0.914 and 0.859. The DCA showed good clinical applicability.
Conclusions:
This predictive nomogram has satisfactory accuracy and discrimination. Therefore, the nomogram can be intuitively and easily used to detect MCI in elderly adults with T2DM.
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