机器学习在大学生自杀意念预测中的应用

  目的  探索机器学习算法在预测大学生是否存在自杀意念中的效果, 并分析大学生自杀意念的危险因素。  方法  选取某高校2021年21 224名在校本科生心理数据。以37项人口学和内外在心理因素为自变量, 以大学生是否存在自杀意念为因变量, 使用支持向量机、随机森林和LightGBM算法分别建立预测模型。将模型应用于测试集上, 以检出率、F1分数和准确率评价预测效果。基于较优模型分析大学生自杀意念的高风险因素。  结果  支持向量机、随机森林和LightGBM模型的检出率依次为0.61, 0.64, 0.69;F1分数依次为0.63, 0.63, 0.64;准确率依次为0.73, 0.73, 0.72。基于较优的LightGBM模型分析大学生自杀意念高风险因素, 按照重要性排序依次为抑郁、年级、性别、绝望、生源地、拥有意义感、对自杀的态度、依赖、家庭经济情况、幻觉妄想症状、焦虑、网络成瘾和人际关系困扰。  结论  LightGBM模型预测大学生是否存在自杀意念相较于支持向量机和随机森林模型有较好的预测效果。

Application of machine learning in the prediction of college students' suicidal ideation

  Objective  To explore the predictive effect of machine learning algorithms on college students'suicidal ideation and to analyze the associated factors of college students'suicidal ideation.  Methods  The mental health data of 21 224 undergraduates was selected from a university in 2021.The independent variables were 37 demographic and internal and external mental health factors.The dependent variable was whether college students had suicidal ideation.Support vector machine, random forest and LightGBM algorithm were used to establish prediction models.The model was used in test set to so as to evaluate the model's prediction effect by using detection rate, F1 score and accuracy rate.Based on the superior model, the high-risk factors of suicidal ideation in college students were analyzed.  Results  The detection rates of support vector machine, random forest, and LightGBM models were 61.0%, 64.0%, 69.0%;F1 scores were 0.63, 0.63, 0.64, and accuracy rates were 73.0%, 73.0%, 72.0%, respectively.Based on the superior LightGBM model, risk factors of suicidal ideation in college students included, depression, grade, gender, despair, place of origin, sense of meaning, attitude toward suicide, dependence, family economic situation, hallucinatory delusion symptoms, anxiety, internet addiction, and interpersonal distress.  Conclusion  The LightGBM model has a better prediction effect than the support vector machine and random forest models.