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Updated: Sep 10, 2025

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

Predictive features analysis and nomogram construction for predicting depression in elderly patients.

Wei Lin¹, Zijun Zhao², Yingshan Yu¹

¹Department of Geriatrics, Fuzhou First General Hospital Affiliated with Fujian Medical University, Fuzhou, Fujian, China.

Frontiers in Psychology

|August 27, 2025

Summary

This summary is machine-generated.

Depression in older adults is common. A new model using Comprehensive Geriatric Assessment (CGA) factors like social support, pain, and daily living abilities can predict depression risk in the elderly.

Keywords:

depression depression screening model elderly patients nomogram predictive features

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Area of Science:

Geriatric Medicine
Psychiatry
Public Health

Background:

Depression is highly prevalent in elderly individuals with chronic diseases and cognitive impairment, leading to significant distress and poor health outcomes.
The aging global population is experiencing a rapid rise in geriatric depression rates.
Comprehensive Geriatric Assessment (CGA) is a multidimensional approach to evaluate elderly patients' medical, psychological, and functional status.

Purpose of the Study:

To identify key factors associated with depression in the elderly using Comprehensive Geriatric Assessment (CGA) data.
To develop and validate a predictive model (nomogram) for geriatric depression risk.
To assess the clinical utility of the developed depression prediction model.

Main Methods:

A cohort of 219 geriatric patients was divided into modeling (153) and validation (66) groups.
Univariate and multivariate regression analyses were performed on patient demographics and CGA results.
A nomogram was constructed integrating identified independent variables influencing depression.

Main Results:

Multivariate analysis identified social support level, pain, anxiety, basic activities of daily living (BADL), and gender as significant predictors of depression.
The nomogram demonstrated good predictive performance with an AUC of 0.867 in the training set and 0.724 in the test set.
The model showed satisfactory calibration, discrimination accuracy, and significant clinical utility via decision curve analysis.

Conclusions:

The developed nomogram, incorporating CGA-derived factors, effectively predicts depression risk in the elderly.
The model exhibits robust performance and holds potential as a valuable clinical screening tool for geriatric depression.
This approach aids in identifying high-risk individuals for targeted interventions in geriatric mental health.