Earticle

초록

영어

Workers who simultaneously engage in dementia caregiving and paid employment represent a critically vulnerable group facing compounded mental health burdens. This study developed a machine learning-based nomogram to predict depression risk among 25,634 family caregivers of home-dwelling dementia patients in South Korea, using data from the 2019-2020 national community health survey. LightGBM identified six key predictors from 135 variables: subjective stress level, subjective health status, cognitive impairment counseling history, economic activity status, gender, and dementia screening history. A Bayesian nomogram derived from multiple logistic regression achieved an AUC of 0.82, accuracy of 0.85, precision of 0.83, recall of 0.85, and F1-score of 0.83 under 10-fold cross-validation. Economically active female caregivers with a history of cognitive impairment counseling and high subjective stress showed a 96% predicted probability of depression, underscoring the urgent need for targeted mental health screening among workers who balance caregiving and employment responsibilities.

목차

Abstract
1. 서론
2. 연구방법
3. 결과
4. 결론
FUND
References

키워드

저자

• 변해원 [ Haewon Byeon | 한국기술교육대학교 미래융합학부 ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회 학술대회논문집(구 한국기계기술학회 학술대회논문집) [Proceedings of KSMT Annual Meeting]

• 간기

  부정기

• 수록기간

  2000~2026

• 십진분류

  KDC 550 DDC 620