Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study.

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 overfit

SUBMITTER: Dong XX 

PROVIDER: S-EPMC11962532 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature