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

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Developing an interpretable machine learning model for screening depression in older adults with functional

Deyan Liu¹, Yuge Tian¹, Min Liu²

¹School of Physical Education, Shandong University, Jinan 250061, China.

Journal of Affective Disorders

|March 6, 2025

Summary

Machine learning models effectively predict depression in older adults with functional disabilities. Key factors include sleep, age, cognition, health status, and lifestyle, aiding early identification and management.

Keywords:

Depression Machine learning Nomogram Older adults with functional disability Risk prediction model SHAP interpretation

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Area of Science:

Gerontology
Psychiatry
Computational Medicine

Background:

Depression is a significant public health concern among older adults, particularly those with functional disabilities.
Identifying reliable predictors and developing accurate risk assessment tools are crucial for timely intervention.

Purpose of the Study:

To develop and validate machine learning-based risk prediction models for depression in older adults with functional disabilities.
To identify key predictors of depression in this vulnerable population.

Main Methods:

Utilized data from 4322 participants (aged 60+) from the 2020 China Health and Retirement Longitudinal Study.
Employed LASSO, univariate, and multivariate logistic regression to identify predictors.
Constructed and evaluated five machine learning models: Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Naive Bayes.

Main Results:

Significant predictors included sleep duration, age, cognitive score, gender, residential area, self-rated health, arthritis, gastrointestinal disease, retirement status, life satisfaction, composite pain, and physical activity.
Gradient Boosting (AUC: 0.76) and Logistic Regression (AUC: 0.75) models showed strong predictive performance.
SHAP interpretation and nomogram visualization enhanced model explainability.

Conclusions:

Machine learning models offer a valuable tool for predicting depression risk in older adults with functional disabilities.
These models can support community screening and clinical decision-making for targeted interventions.