Machine learning models for predicting depression in Korean young employees

Kim, Suk‐Sun; Gil, Minji; Min, Eun Jeong

doi:10.3389/fpubh.2023.1201054

Cited by 4 publications

(1 citation statement)

References 71 publications

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“…Remarkably, the logistic regression model, a key focus of our study, exhibits comparable effectiveness when benchmarked against commonly used ML models. This finding aligns with broader research on depression, where logistic regression has consistently demonstrated either superior or comparable performance compared to alternative ML models ( 31–33 ). Our observation prompts consideration of two pivotal factors that contribute to this alignment.…”

Section: Discussionsupporting

confidence: 86%

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

Sadjadpour,

Hosseinichimeh,

Abedi

et al. 2024

Front. Public Health

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IntroductionNeonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences.ObjectiveOur objective is to compare the performance of the traditional logistic regression with other machine learning (ML) models in identifying parents who are more likely to have depression symptoms to prioritize screening of at-risk parents. We used data obtained from parents of infants discharged from the NICU at Children’s National Hospital (n = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors.Study designOur study design optimized eight ML algorithms – Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network – to identify the main risk factors associated with parental depression. We compared models based on the area under the receiver operating characteristic curve (AUC), positive predicted value (PPV), sensitivity, and F-score.ResultsThe results showed that all eight models achieved an AUC above 0.8, suggesting that the logistic regression-based model’s performance is comparable to other common ML models.ConclusionLogistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.

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Section: Discussionsupporting

confidence: 86%

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

Sadjadpour,

Hosseinichimeh,

Abedi

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

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Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review

Todd,

Orr,

Gamage

et al. 2025

Computers in Biology and Medicine

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Factors Influencing Anxiety in MZ Generation Workers: Occupational Stress, Social Problem Solving, Work as Meaning, and Work-Life Balance

Shin,

Kim

2024

J Korean Acad Psychiatr Ment Health Nurs

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Purpose: This study aimed to examine factors influencing the anxiety levels of MZ generation workers, including occupational stress, social problem solving, work as meaning, and work-life balance.Methods: This study used secondary data from 503 MZ generation workers which was originally collected in a mental health study of workers applying machine learning techniques. Data were analyzed using descriptive statistics, independent t-tests, one-way ANOVA, Pearson’s correlation analysis, and stepwise multiple regression analysis with SPSS 26.Results: Results indicated that the job demand and job insecurity subscales of occupational stress, negative problem orientation, avoidance style, and impulsive & careless style of social problem solving scale were identified as significant factors that increased anxiety. In contrast, the job control subscale of occupational stress, work-life balance, the greater good motivation subscale of work as meaning, and positive problem orientation of social problem solving scale were identified as important factors that reduced anxiety. These factors collectively explained approximately 49% of the variance in anxiety among MZ generation workers.Conclusion: The findings suggest that it is essential to manage occupational stress with an optimistic perspective, develop social problem solving skills with a sense of meaning at work and maintain a great work-life balance to reduce anxiety among MZ generation workers.

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Machine learning models for predicting depression in Korean young employees

Cited by 4 publications

References 71 publications

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review

Factors Influencing Anxiety in MZ Generation Workers: Occupational Stress, Social Problem Solving, Work as Meaning, and Work-Life Balance

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