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Updated: Dec 27, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Establishment of a Risk Prediction Model for Mild Cognitive Impairment among Elderly Chinese
1Xiaoping Wang, Department of Neurology, Shanghai General Hospital of Nanjing Medical University, Tongren Hospital Shanghai Jiao Tong University School of Medicine, No.1111 Xianxia Road, Changning District, Shanghai 200336, P.R China, Tel.: +86 021 52039999, Fax: +86 021 63243755,
Background:
Mild cognitive impairment (MCI) is a transitional stage of cognitive function between normal aging and dementia. Substantial variations in the prevalence of MCI in different countries have been studied including China. In this study, we established a prediction system to assess the risk of MCI among the elderly in China.
Methods:
The Rothman-Keller model was conducted on the basis of the risk factors of MCI obtained by the combined results of a meta-analysis. The accuracy of the model was verified using actual population data.
Results:
A total of 1826 subjects as a verification set were enrolled in this study in February 2019. There were statistically significant differences in the combined results of 10 risk factors including hypertension, diabetes, educational level, hyperlipidemia, smoking, physical exercise, living alone, stroke, drinking and heart disease (P<0.05). The area under the curve (AUC) of the actual data and the predictive results of this model was 0.859 (95%CI: 0.812-0.906, P<0.05), the sensitivity was 86.6% and the specificity was 76.5%.
Conclusions:
This model performs an effective prediction that may be applied to the primary prevention for patients with MCI, helping to reduce the risk of MCI.
