Universiti Sains Malaysia, Malaysia.
National University of Malaysia Medical Centre, Malaysia.
Health Informatics J. 2021 Jan-Mar;27(1):1460458221989395. doi: 10.1177/1460458221989395.
Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts.
目前用于预测自杀企图的自杀风险评估既耗时，预测价值又低，可靠性也不足。本文旨在使用机器学习算法为抑郁症患者开发自杀企图预测模型，并对单预测模型和集成预测模型在区分有自杀企图和无自杀企图的抑郁症患者方面进行比较研究。我们应用并训练了八种不同的机器学习算法，使用的数据集由 75 名被诊断为抑郁症的患者组成。通过三折交叉验证，递归特征消除用于减少特征。集成预测模型的表现优于单预测模型。与其他机器学习算法相比，投票和装袋法的准确率最高，达到 92%。我们的研究结果表明，自杀史、宗教信仰、种族、自杀意念和临床抑郁严重程度是预测自杀企图的有用因素。
