基于多因素logistic回归、决策树以及神经网络，分别建立肺癌患者胸腔镜术后肺部感染预测模型，并比较三个模型性能。

选取2022年10月1日至2025年3月31日在镇江市第三人民医院、句容市人民医院、丹阳市第三人民医院行胸腔镜下肺部切除术的肺癌住院患者，收集患者基本信息、体征检查、既往病史、肺功能指数、手术情况、肿瘤情况等。根据是否发生肺部感染将患者分为肺部感染组和非肺部感染组，单因素分析比较两组差异。将单因素分析中有统计学意义的因素作为自变量，纳入多因素logistic回归、决策树和神经网络，构建预测模型，并比较三种模型的灵敏度、特异度、约登指数和受试者工作特征（ROC）曲线下面积（AUC）。

共计1 262例患者纳入研究，其中230例发生肺部感染，发生率为18.22%。多因素logistic回归显示年龄（≥60岁）、性别、吸烟史、慢性肺部疾病史、住院时间（≥10天）为肺癌患者胸腔镜术后发生肺部感染的独立危险因素，决策树模型得到4个解释变量，即年龄（≥60岁）、血清白蛋白水平（<35 g/L）、住院时间（≥10天）、吸烟史，神经网络模型提示前五位危险因素为年龄（≥60岁）、血清白蛋白水平（<35 g/L）、慢性肺部疾病史、住院时间（≥10天）、吸烟史。以上三种模型的准确度分别为84.9%、81.5%、86.7%，灵敏度分别为77.2%、73.2%、75.4%，特异度分别为80.3%、76.8%、82.7%，约登指数分别为0.575、0.520、0.581，AUC分别为0.831、0.796、0.857（P<0.05）。

与多因素logistic回归模型及决策树模型相比，神经网络模型对肺癌患者胸腔镜术后肺部感染的预测性能更佳。

To establish and compare three prediction models for pulmonary infection after thoracoscopic surgery in lung cancer patients based on multivariate logistic regression, decision tree, and neural network.

Patients with lung cancer who underwent thoracoscopic lung resection in Zhenjiang Third People’s Hospital, Jurong People’s Hospital, and Danyang Third People’s Hospital from October 1, 2022 to March 31, 2025 were selected. Data on patient demographics, physical examination findings, past medical history, pulmonary function indices, surgical conditions, and tumor conditions of the patients were collected. The patients were divided into a pulmonary infection group and a non-pulmonary infection group based on the occurrence of pulmonary infection. The differences between the two groups were compared through univariate analysis. The variables with statistical significance in the univariate analysis were selected as independent variables for multivariate logistic regression, decision tree, and neural network to construct the prediction models, and the sensitivity, specificity, Youden index, and area under the receiver operating characteristic (ROC) curve (AUC) of the three models were compared.

A total of 1 262 patients were included in the study, of whom 230 cases had pulmonary infection, with an incidence of 18.22%. Multivariate logistic regression showed that age (≥60 years), gender, smoking history, history of chronic pulmonary diseases, and hospitalization time (≥10 days) were independent risk factors for postoperative pulmonary infection. The decision tree model obtained 4 explanatory variables: age (≥60 years), serum albumin level (<35 g/L), hospitalization time (≥10 days), and smoking history. The neural network model suggested that the top five risk factors were age (≥60 years), serum albumin level (<35 g/L), history of chronic pulmonary diseases, hospitalization time (≥10 days), and smoking history. The accuracy of the three models was 84.9%, 81.5%, and 86.7%, respectively. The sensitivity was 77.2%, 73.2%, and 75.4%; specificity was 80.3%, 76.8%, and 82.7%; Youden index was 0.575, 0.520, and 0.581; and the AUC was 0.831, 0.796, and 0.857, respectively (all P<0.05).

Compared with the multivariate logistic regression model and the decision tree model, the neural network model has better predictive performance for pulmonary infection after thoracoscopic surgery in lung cancer patients.