TY - JOUR
T1 - Machine learning models for predicting risk of depression in Korean college students
T2 - Identifying family and individual factors
AU - Gil, Minji
AU - Kim, Suk Sun
AU - Min, Eun Jeong
N1 - Publisher Copyright:
Copyright © 2022 Gil, Kim and Min.
PY - 2022/11/17
Y1 - 2022/11/17
N2 - 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 to predict the risk of depression in college students and identify important family and individual factors. Methods: This study predicted college students at risk of depression and identified 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 (SLR), support vector machine (SVM), and random forest (RF), was compared. Results: The three ML models showed excellent prediction capabilities. The RF model showed the best performance. It revealed five significant factors responsible for depression: self-perceived mental health of college students, neuroticism, fearful-avoidant attachment, family cohesion, and mother's depression. Additionally, the logistic regression model identified five factors responsible for depression: the severity of cancer in the father, the severity of respiratory diseases in the mother, the self-perceived mental health of college students, conscientiousness, and neuroticism. Discussion: These findings demonstrated the ability of ML models to accurately predict the 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.
AB - 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 to predict the risk of depression in college students and identify important family and individual factors. Methods: This study predicted college students at risk of depression and identified 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 (SLR), support vector machine (SVM), and random forest (RF), was compared. Results: The three ML models showed excellent prediction capabilities. The RF model showed the best performance. It revealed five significant factors responsible for depression: self-perceived mental health of college students, neuroticism, fearful-avoidant attachment, family cohesion, and mother's depression. Additionally, the logistic regression model identified five factors responsible for depression: the severity of cancer in the father, the severity of respiratory diseases in the mother, the self-perceived mental health of college students, conscientiousness, and neuroticism. Discussion: These findings demonstrated the ability of ML models to accurately predict the 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.
KW - college student
KW - depression
KW - family
KW - machine learning
KW - risk factors
UR - http://www.scopus.com/inward/record.url?scp=85143342305&partnerID=8YFLogxK
U2 - 10.3389/fpubh.2022.1023010
DO - 10.3389/fpubh.2022.1023010
M3 - Article
C2 - 36466485
AN - SCOPUS:85143342305
SN - 2296-2565
VL - 10
JO - Frontiers in Public Health
JF - Frontiers in Public Health
M1 - 1023010
ER -