Qiang Qi, Hu Jinsheng, Chen Xianke, Guo Weihua, Yang Qingshuo, Wang Zhijun, Liu Zhihong, Zhang Ya, Li Qi
Department of Psychology, Liaoning Normal University, Dalian, China.
Front Public Health. 2025 Jul 9;13:1606947. doi: 10.3389/fpubh.2025.1606947. eCollection 2025.
In this study, machine learning was used to assess the prediction of the magnitude of depression changes in college students based on various psychological variable information.
A group of college students from a certain school completed two assessments in October 2021 and March 2022, respectively. We collected baseline levels of depression, demographic variables, parenting styles, college students' mental health information, personality information, coping styles, SCL-90, and social support information. We applied logistic regression, random forest, support vector machine, and k-nearest neighbor machine learning methods to predict the magnitude of depression changes in college students. We selected the best-performing model and outputted the importance of features collected at different time points.
Whether it is predicting the magnitude of positive changes or negative changes in depression, support vector machines (SVM) had the best prediction performance (with an accuracy of 89.4% for predicting negative changes in depression and an accuracy of 91.9% for predicting positive changes in depression). The baseline level of depression, father's emotional expression, and mother's emotional expression were all important predictors for predicting the negative and positive changes in depression among college students.
Machine learning models can predict the extent of depression changes in college students. The baseline level of depression, as well as the emotional state of both fathers and mothers, play a significant role in predicting the negative and positive changes associated with depression in college students. This provides new insights and methods for future psychological health research and practice.
在本研究中，机器学习被用于基于各种心理变量信息评估大学生抑郁变化程度的预测。
某学校的一组大学生分别于2021年10月和2022年3月完成了两项评估。我们收集了抑郁的基线水平、人口统计学变量、养育方式、大学生心理健康信息、人格信息、应对方式、症状自评量表（SCL - 90）以及社会支持信息。我们应用逻辑回归、随机森林、支持向量机和k近邻机器学习方法来预测大学生抑郁变化的程度。我们选择了表现最佳的模型，并输出了在不同时间点收集的特征的重要性。
无论是预测抑郁的正向变化还是负向变化程度，支持向量机（SVM）都具有最佳的预测性能（预测抑郁负向变化的准确率为89.4%，预测抑郁正向变化的准确率为91.9%）。抑郁的基线水平、父亲的情感表达和母亲的情感表达都是预测大学生抑郁负向和正向变化的重要预测因素。
机器学习模型可以预测大学生抑郁变化的程度。抑郁的基线水平以及父母双方的情绪状态在预测大学生抑郁相关的负向和正向变化中起着重要作用。这为未来的心理健康研究和实践提供了新的见解和方法。
