The purpose of this study was to explore the establishment of an auxiliary scoring model for patients with acute pulmonary embolism (APE) complicated with atrial fibrillation (AF) based on random forest (RF) and its application effect. A retrospective analysis was performed on the general data, underlying diseases, laboratory indicators, and cardiac indicators of 100 patients with APE admitted to our hospital from 2018 to 2021. The occurrence of atrial fibrillation in patients with pulmonary embolism was taken as a categorical variable, and the general data, underlying diseases, laboratory indicators, and cardiac indicators were taken as input variables. Then, the risk auxiliary scoring model for patients with APE complicated with AF was established based on RF and logistic regression. Finally, the accuracy, sensitivity, specificity, recall rate, accuracy, F1 value, and the receiver operator characteristic (ROC) curve were used to evaluate the predictive value of the models. After statistical analysis, the optimal node value was 3 and the optimal number of decision trees was 500 in the RF model. The importance of predictors in descending order were Hcy, diabetes mellitus, FT3 level, UA level, left atrial diameter, hypertension, and smoking history. The prediction accuracy of the RF model was 0.934, sensitivity 0.966, specificity 0.876, recall rate 0.9660, accuracy 0.934, and F1 value 0.950. The logistic regression model prediction accuracy was 0.816, sensitivity 0.915, specificity 0.125, recall rate 0.902, accuracy 0.811, and F1 value 0.896. The RF model and logistic regression prediction model AUC values were 0.984 and 0.883, respectively. From this, we conclude that the RF model was better than the logistic regression model in predicting AF in APE patients. So, the RF model had the clinical application value.

中文翻译：

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建立基于随机森林的急性肺栓塞合并房颤患者辅助评分模型


本研究旨在探讨基于随机森林（RF）的急性肺栓塞（APE）合并房颤（AF）患者辅助评分模型的建立及其应用效果。对2018年至2021年我院收治的100例APE患者的一般资料、基础疾病、实验室指标、心脏指标进行回顾性分析。以肺栓塞患者心房颤动的发生情况作为分类变量，以一般资料、基础疾病、实验室指标、心脏指标作为输入变量。然后，基于RF和Logistic回归建立APE合并房颤患者的风险辅助评分模型。最后，利用准确率、灵敏度、特异度、召回率、准确率、F1值和受试者工作特征（ROC）曲线来评估模型的预测价值。经过统计分析，RF模型中最优节点值为3，最优决策树数量为500。预测因素的重要性按降序排列为Hcy、糖尿病、FT3水平、UA水平、左心房直径、高血压和吸烟史。 RF模型的预测准确度为0.934，敏感性0.966，特异性0.876，召回率0.9660，准确度0.934，F1值0.950。逻辑回归模型预测准确度为0.816，灵敏度0.915，特异度0.125，召回率0.902，准确度0.811，F1值0.896。 RF模型和逻辑回归预测模型AUC值分别为0.984和0.883。由此，我们得出结论，RF 模型在预测 APE 患者的 AF 方面优于逻辑回归模型。 因此，RF模型具有临床应用价值。