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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting cognitive function among Chinese community-dwelling older adults: A supervised machine learning approach.

Xin Ye¹, Xinfeng Wang², Yu Wang³

¹Institute for Global Public Policy, Fudan University, Shanghai 200433, China; LSE-Fudan Research Centre for Global Public Policy, Fudan University, Shanghai 200433, China.

Preventive Medicine

|May 11, 2025

Summary

Early identification of cognitive impairment in Chinese older adults is possible using a precise prediction model. This tool aids timely intervention and supports successful cognitive aging.

Keywords:

Cognitive function Machine learning Older adults Risk prediction

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Area of Science:

Gerontology
Cognitive Science
Public Health

Background:

Early identification of cognitive impairment is crucial for timely intervention and promoting healthy cognitive aging in older adults.
Developing precise prediction models with minimal input variability is essential for community-dwelling populations.

Purpose of the Study:

To develop accurate prediction models for current and future cognitive function in Chinese community-dwelling older adults.
To identify key features for predicting cognitive status using a nationally representative dataset.

Main Methods:

Utilized data from 13,906 older adults (aged 60+) from the China Health and Retirement Longitudinal Study (CHARLS) (2011-2020).
Employed Gradient Boosting Classifier (GBC) and gradient boosting regressor (GBR) models to predict current and future cognitive function.
Selected ten features including education, social participation, and daily living activities for model development.

Main Results:

The GBC model achieved an Area Under the ROC curve of 0.832 for classifying cognitive impairment.
The GBR model yielded a Root Mean Square Error (RMSE) of 3.356 for continuous cognitive function prediction.
Models incorporating current cognitive state showed superior performance in forecasting future cognition.

Conclusions:

Developed a practical tool for early cognitive impairment identification in community settings.
Supports timely interventions to facilitate successful cognitive aging.
Highlights the utility of machine learning models in geriatric cognitive health assessment.