Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study

Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follo...

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Veröffentlicht in:International journal of public health Jg. 68; S. 1605322
Hauptverfasser: Liu, Haihong, Zhang, Xiaolei, Liu, Haining, Chong, Sheau Tsuey
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 19.01.2023
Schlagworte:
Aged
Cognitive Dysfunction - diagnosis
Cognitive Dysfunction - epidemiology
cognitive impairment
Cross-Sectional Studies
dementia
East Asian People
Humans
Longitudinal Studies
longitudinal study
Machine Learning
Middle Aged
middle-aged and older Chinese
Public Health Archive
random forest
middle-aged and older Chinese
dementia
cognitive impairment
longitudinal study
random forest
machine learning
ISSN:1661-8564, 1661-8556, 1661-8564
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Zusammenfassung:Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment. Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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This Original Article is part of the IJPH Special Issue “Public Health and Primary Care, is 1+1=1?”
Edited by: Gabriel Gulis, University of Southern Denmark, Denmark
Reviewed by: Alessandra Costanza, University of Geneva, Switzerland
ISSN:1661-8564
1661-8556
1661-8564
DOI:10.3389/ijph.2023.1605322