Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study
- Author:
Xing-Xuan DONG
1
;
Jian-Hua LIU
;
Tian-Yang ZHANG
;
Chen-Wei PAN
;
Chun-Hua ZHAO
;
Yi-Bo WU
;
Dan-Dan CHEN
Author Information
- Publication Type:Original Article
- From:Psychiatry Investigation 2025;22(3):267-278
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objective:Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic.
Methods:Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC).
Results:LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models.
Conclusion:Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.
