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

• 발행기관: 대한신경정신의학회
• 저자명: Xing-Xuan Dong Jian-Hua Liu Tian-Yang Zhang Chen-Wei Pan Chun-Hua Zhao Yi-Bo Wu Dan-Dan Chen
• 간행물 정보: 『Psychiatry Investigation』제22권 제3호, 267~278쪽, 전체 12쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2025.03.18

국문 초록

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.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

키워드

Logistic regression Machine learning Depressive symptoms Risk factors.