ArticlePsychiatry investigation2025
Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study.
Article in Psychiatry investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Integrating epidemiologic modeling and explainable machine learning to predict and identify factors associated with self-reported depression among adults in Tennessee, United States.Discover mental health · 2026Article
- Developing a prediction model for persistent airflow limitation in asthmatic children.Journal of thoracic disease · 2025Article
- Leveraging Explainable AI to Identify Determinants of Lifetime HIV Testing Among Adults in Tennessee, United States: Evidence for Targeted Public Health Strategies From BRFSS 2023.Journal of primary care & community healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveMachine 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.
methodsAnalyses 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).
resultsLR 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.
conclusionCompared 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.
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