AUTHOR=Gil Minji , Kim Suk-Sun , Min Eun Jeong 

TITLE=Machine learning models for predicting risk of depression in Korean college students: Identifying family and individual factors

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.1023010

DOI=10.3389/fpubh.2022.1023010

ISSN=2296-2565

ABSTRACT=Background: Depression is one of the most prevalent mental illnesses among college students worldwide. Using the family triad dataset, this study investigated machine learning (ML) models with the goal of predicting risk of depression in college students and identifying the important family and individual factors.
Methods: To predict college students at risk of depression and to identify significant family and individual factors in 171 family data (171 fathers, mothers, and college students), the prediction accuracy of three ML models, sparse logistic regression, support vector machine, and random forest was compared.
Results: The three ML models showed excellent prediction capabilities. The random forest model showed the best performance. It revealed five significant factors: college students’ self-perceived mental health, neuroticism, fearful-avoidant attachment, family cohesion, and mother’s depression. Additionally, the logistic regression model identified five factors: father’s severity of cancer, mother’s severity of respiratory diseases, college students’ self-perceived mental health, conscientiousness, and neuroticism.
Discussion: These findings demonstrate the ability of ML models to accurately predict risk of depression and identify family and individual factors related to depression among Korean college students. With recent developments and ML applications, our study can improve intelligent mental healthcare systems to detect early depressive symptoms and increase access to mental health services.