Suicide rates among military-connected populations have increased over the past 15 years. Meta-analytic studies indicate prediction of suicide outcomes is lacking. Machine-learning approaches have been promoted to enhance classification models for suicide-related outcomes. In the present study, we compared the performance of three primary machine-learning approaches (i.e., elastic net, random forests, stacked ensembles) and a traditional statistical approach, generalized linear modeling (i.e., logistic regression), to classify suicide thoughts and behaviors using data from the Military Suicide Research Consortium’s Common Data Elements (CDE; n = 5,977–6,058 across outcomes). Models were informed by (a) selected items from the CDE or (b) factor scores based on exploratory and confirmatory factor analyses on the selected CDE items. Results indicated similar classification performance across models and sets of features. In this study, we suggest the need for robust evidence before adopting more complex classification models and identify measures that are particularly relevant in classifying suicide-related outcomes.

在过去的15年中，与军事有联系的人群中的自杀率上升了。荟萃分析研究表明，对自杀结果的预测缺乏。促进了机器学习方法，以增强与自杀有关的结果的分类模型。在本研究中，我们比较了三种主要的机器学习方法（即弹性网，随机森林，堆叠式集成）和传统统计方法（广义线性建模（即逻辑回归））的性能，以对自杀的思想和行为进行分类。使用军事自杀研究联合会通用数据元素（CDE; n=整个结果中的5,977–6,058）。通过以下方式为模型提供信息：（a）从CDE中选择的项目，或（b）基于对所选CDE项目进行的探索性和确认性因素分析的因子得分。结果表明，跨模型和特征集的分类性能相似。在这项研究中，我们建议在采用更复杂的分类模型之前，需要有力的证据，并确定与自杀相关结果分类特别相关的措施。