Comparison of Different Machine Learning Algorithms in the Mental Health Assessment of College Students

Q3 Decision Sciences
Yongsen Cai;Danling Lin;Qing Lu
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引用次数: 0

Abstract

This paper assesses college students' mental health based on the symptom checklist 90 (SCL-90). In view of the assessment data processing and analysis, the performance of different machine learning algorithms, including random forest (RF), LightGBM3, extreme gradient boosting (XGBoost), in the classification of college students' mental health samples was compared. Moreover, the effect of different hyperparameter optimization methods (grid search, Bayesian optimization, and particle swarm optimization) was compared. The experiment on the SCL-90 assessment dataset found that the optimization effect of grid search was poor, and the highest F1 value and area under the curve (AUC) of the RF algorithm were 0.8914 and 0.9384, respectively, the highest F1 and AUC values of the XGBoost algorithm were 0.9166 and 0.9551, respectively. The LightGBM algorithm optimized by particle swarm optimization showed the best performance in the classification of mental health samples, with an F1 value of 0.9790 and an AUC of 0.9945. It also achieved optimal results when compared to machine learning algorithms such as naive Bayes and the support vector machines. The results prove the reliability and accuracy of the particle swarm optimization-improved Light-GBM algorithm in the analysis of college students' mental health assessment data. The algorithm can be applied in practice to provide an effective tool for the analysis of the mental health assessment data of college students.
不同机器学习算法在大学生心理健康评估中的比较
采用症状自评量表(SCL-90)对大学生的心理健康状况进行测评。针对评估数据的处理与分析,比较了随机森林(random forest, RF)、LightGBM3、极限梯度提升(extreme gradient boosting, XGBoost)等不同机器学习算法在大学生心理健康样本分类中的表现。对比了不同超参数优化方法(网格搜索、贝叶斯优化和粒子群优化)的效果。在SCL-90评估数据集上的实验发现,网格搜索的优化效果较差,RF算法的最高F1值和曲线下面积(AUC)分别为0.8914和0.9384,XGBoost算法的最高F1值和AUC分别为0.9166和0.9551。采用粒子群算法优化的LightGBM算法对心理健康样本的分类效果最好,F1值为0.9790,AUC为0.9945。与朴素贝叶斯和支持向量机等机器学习算法相比,它也获得了最佳结果。结果证明了粒子群优化改进的Light-GBM算法在大学生心理健康评估数据分析中的可靠性和准确性。该算法可应用于实际,为大学生心理健康评估数据的分析提供有效工具。
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来源期刊
Journal of ICT Standardization
Journal of ICT Standardization Computer Science-Information Systems
CiteScore
2.20
自引率
0.00%
发文量
18
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