Confusion matrix (CPH).

BackgroundIn recent years, remarkable progress has been made in the use of machine learning, especially in analyzing prognosis survival data. Traditional prediction models cannot identify interrelationships between factors, and the predictive accuracy is lower. This study aimed to construct Bayesian network models using the tree augmented naïve algorithm in comparison with the Cox proportional hazards model.MethodsA Bayesian network model and a Cox proportional hazards model were constructed to analyze the prognostic factors of endometrial cancer. In total, 618 original cases obtained from the Surveillance, Epidemiology, and End Results database were used to construct the Bayesian network model, which was compared with the traditional Cox proportional hazards model by analyzing prognostic factors. External validation was performed using a dataset from The First Affiliated Hospital of Shandong First Medical University.ResultsThe predictive accuracy, area under the receiver operating characteristic curve, and concordance index for the Bayesian network model were 74.68%, 0.787, and 0.72, respectively, compared to 68.83%, 0.723, and 0.71, respectively, for the Cox proportional hazards model. Tumor size was the most important factor for predicting survival, followed by lymph node metastasis, distant metastasis, chemotherapy, lymph node resection, tumor stage, depth of invasion, tumor grade, histological type, age, primary tumor site, radiotherapy and surgical sequence, and radiotherapy.ConclusionThe findings indicate that the Bayesian network model is preferable to the Cox proportional hazards model for predicting survival in patients with endometrial cancer.

研究背景：近年来，机器学习技术的应用取得了显著进展，尤其在预后生存数据分析领域。传统预测模型无法识别各因素间的内在关联，且预测精度较低。本研究旨在采用树增强朴素算法（tree augmented naïve algorithm）构建贝叶斯网络（Bayesian network）模型，并与Cox比例风险模型（Cox proportional hazards model）进行对比。
研究方法：本研究构建贝叶斯网络模型与Cox比例风险模型，以分析子宫内膜癌的预后影响因素。本研究从监测、流行病学与最终结果（Surveillance, Epidemiology, and End Results）数据库中获取618例原始病例，用于构建贝叶斯网络模型，并通过分析预后影响因素与传统Cox比例风险模型进行对比。本研究采用山东第一医科大学第一附属医院的数据集进行外部验证。
研究结果：贝叶斯网络模型的预测精度、受试者工作特征曲线下面积（area under the receiver operating characteristic curve）及一致性指数（concordance index）分别为74.68%、0.787和0.72；而Cox比例风险模型的对应指标分别为68.83%、0.723和0.71。影响患者生存预测的最重要因素为肿瘤大小，其次为淋巴结转移、远处转移、化疗、淋巴结清扫、肿瘤分期、浸润深度、肿瘤分级、组织学类型、年龄、原发肿瘤部位、放疗与手术顺序以及放疗。
研究结论：本研究结果表明，在子宫内膜癌患者的生存预测中，贝叶斯网络模型优于Cox比例风险模型。