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Updated: Oct 21, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Construction and validation of a predictive model for sleep disorders among rural older adults with mild cognitive
Teng Yang1, Jiajing Chi1, Jia Song2
1Department of Nursing, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
Objective:
to construct and validate a risk assessment model for sleep disturbance among rural older adults with mild cognitive impairment (MCI), to support early screening and targeted intervention.
Design:
a cross-sectional study using data from the 2024 baseline survey of the Active Aging Cohort in Rural Areas, Shandong Province, China.
Subjects:
1,049 rural older adults aged ≥ 60 years with MCI.
Methods:
demographic, lifestyle, disease history, and psychological factors were collected through face-to-face survey questionnaires and health examinations. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Logistic regression identified independent predictors, which were used to build a nomogram. Model performance was evaluated with ROC curve, calibration curve, decision curve analysis, and bootstrap validation.
Results:
the prevalence of sleep disturbance was 54.2%. Sex, hypertension, self-rated health, and depression were identified as independent predictors. The nomogram demonstrated good discrimination (AUC = 0.759, 95%CI: 0.731-0.778) and calibration, with internal validation confirming robustness.
