Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study.

Journal: BMC psychiatry

PMID: 39953491

Abstract

BACKGROUND: Given the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common mental disorder among older adults. This study aims to establish an effective model for predicting depression risks among disabled elderly individuals.

Authors

Shanshan Hong

Center of Health Administration and Development Studies, Hubei University of Medicine, NO. 30 Ren Min South Road, Maojian District, Shiyan, Hubei, 442000, China.
Bingqian Lu

Center of Health Administration and Development Studies, Hubei University of Medicine, NO. 30 Ren Min South Road, Maojian District, Shiyan, Hubei, 442000, China.
Shaobing Wang

Center of Health Administration and Development Studies, Hubei University of Medicine, NO. 30 Ren Min South Road, Maojian District, Shiyan, Hubei, 442000, China. wshaobing121@qq.com.
Yan Jiang

Department of Nursing/Evidence-based Nursing Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Keywords

Activities of Daily Living Aged Aged, 80 and over China Depression Female Humans Logistic Models Longitudinal Studies Machine Learning Male Middle Aged Persons with Disabilities Risk Assessment Risk Factors

External Resources

View on PubMed Access via DOI PubMed (39953491)

Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study.

Abstract

Authors

Keywords

External Resources

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