Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1,090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 ($\pm$7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 ($\pm$2) compared to 26 ($\pm$1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients.

尽管在急性缺血性卒中患者中进行血管内治疗具有总体上的总体有益效果，但仍有近三分之一的患者仍然出现严重的残疾或死亡。这些可能无法从治疗中受益的患者先前已通过传统的逻辑回归模型进行了识别，这可能会过分简化特征和结果之间的关系，或者可能难以解释机器学习技术。我们开发并评估了一种新的模糊决策树进化算法，以准确识别血管内治疗后预后较差的患者，该算法定义为具有改良的Rankin Scale评分（mRS），等于或大于5。创建的决策树的优势在于易于理解的模型易于理解，从而使其预测易于向患者和从业人员解释。对预期结果的原因的见解可以鼓励实践中的接受和适应，并有助于在治疗后管理期望。我们将我们提出的方法与CART（基准决策树算法）进行了分类准确性和可解释性的比较。模糊决策树的性能明显优于CART：对训练组的平均1,090名患者和测试组的273名患者进行5倍交叉验证，模糊决策树对平均77名（标准差为7）的患者进行了错误分类，而对83名患者进行了错误分类（$\pm$7）使用CART。模糊决策树中的平均节点数（决策和叶节点）为11（$\pm$2）相较于26（$\pm$1）用于CART决策树。相对于CART，平均准确度为72％，节点少得多，因此开发的模糊决策树进化算法可用于深入了解患者特征的预测价值，并有助于开发更准确的医疗结果预测方法，并提高为从业者和患者提供清晰的信息。