Predicting Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms

Suicide is a major global health concern and a prominent cause of death in adolescents. Previous research on suicide prediction has mainly focused on clinical or adult samples. To prevent suicides at an early stage, however, it is important to screen for risk factors in a community sample of adolescents. We compared the accuracy of logistic regressions, elastic net regressions and gradient boosting machines in predicting suicide attempts by 17-year-olds in the Millennium Cohort Study (N = 7,347), combining a large set of self- and other-reported variables from different categories. Both machine learning algorithms outperformed logistic regressions and achieved similar balanced accuracies (.76 when using data 3 years before the self-reported lifetime suicide attempts and .85 when using data from the same measurement wave). We identified essential variables that should be considered when screening for suicidal behavior. Finally, we discuss the usefulness of complex machine learning models in suicide prediction.

自杀是全球范围内的重大公共卫生问题，同时也是青少年死亡的主要原因。以往关于自杀预测的研究主要集中于临床或成人样本。然而，为了在早期阶段预防自杀，对青少年社区样本中的风险因素进行筛查至关重要。本研究对比了逻辑回归、弹性网络回归和梯度提升机在预测千禧年队列研究（N = 7,347）中17岁青少年自杀尝试的准确性，结合了来自不同类别的大量自我报告和其他报告变量。两种机器学习算法均优于逻辑回归，并实现了相似的平衡准确率（在使用3年前自我报告终身自杀尝试数据时为0.76，在使用同一测量波次的数据时为0.85）。我们确定了在筛查自杀行为时应考虑的关键变量。最后，我们讨论了复杂机器学习模型在自杀预测中的实用性。