About 30% of patients drop out of cognitive–behavioural therapy (CBT), which has implications for psychiatric and psychological treatment. Findings concerning drop out remain heterogeneous.

This paper aims to compare different machine-learning algorithms using nested cross-validation, evaluate their benefit in naturalistic settings, and identify the best model as well as the most important variables.

The data-set consisted of 2543 out-patients treated with CBT. Assessment took place before session one. Twenty-one algorithms and ensembles were compared. Two parameters (Brier score, area under the curve (AUC)) were used for evaluation.

The best model was an ensemble that used Random Forest and nearest-neighbour modelling. During the training process, it was significantly better than generalised linear modelling (GLM) (Brier score: d = –2.93, 95% CI (−3.95, −1.90)); AUC: d = 0.59, 95% CI (0.11 to 1.06)). In the holdout sample, the ensemble was able to correctly identify 63.4% of cases of patients, whereas the GLM only identified 46.2% correctly. The most important predictors were lower education, lower scores on the Personality Style and Disorder Inventory (PSSI) compulsive scale, younger age, higher scores on the PSSI negativistic and PSSI antisocial scale as well as on the Brief Symptom Inventory (BSI) additional scale (mean of the four additional items) and BSI overall scale.

Machine learning improves drop-out predictions. However, not all algorithms are suited to naturalistic data-sets and binary events. Tree-based and boosted algorithms including a variable selection process seem well-suited, whereas more advanced algorithms such as neural networks do not.

大约 30% 的患者放弃认知行为治疗 (CBT)，这对精神病学和心理治疗具有影响。关于辍学的调查结果仍然存在差异。

本文旨在使用嵌套交叉验证来比较不同的机器学习算法，评估它们在自然环境中的好处，并确定最佳模型和最重要的变量。

该数据集由 2543 名接受 CBT 治疗的门诊患者组成。评估在第一节之前进行。比较了二十一种算法和集成。使用两个参数（Brier评分、曲线下面积（AUC））进行评估。

最好的模型是使用随机森林和最近邻建模的集成。在训练过程中，明显优于广义线性模型（GLM）（Brier评分： d = –2.93，95% CI（−3.95，−1.90））； AUC： d = 0.59，95% CI（0.11 至 1.06））。在保留样本中，整体能够正确识别 63.4% 的患者病例，而 GLM 只能正确识别 46.2%。最重要的预测因素是教育程度较低、人格风格和障碍量表（PSSI）强迫量表得分较低、年龄较小、PSSI消极和PSSI反社会量表以及简短症状量表（BSI）附加量表得分较高。四个附加项目的平均值）和 BSI 总体量表。

机器学习改善了辍学预测。然而，并非所有算法都适合自然数据集和二进制事件。包括变量选择过程的基于树的增强算法似乎非常适合，而神经网络等更高级的算法则不然。