基于3种机器学习算法构建宫颈癌术后尿潴留风险预测模型OACSTPCD
Construction of risk prediction model of postoperative urinary retention based on three machine learning algorithms
目的:运用决策树、逻辑回归和支持向量机构建宫颈癌根治性切除术后尿潴留风险预测模型并比较性能,为评估及预防宫颈癌术后尿潴留提供参考依据.方法:回顾性收集459例宫颈癌根治性切除术病人的临床资料,采用决策树、支持向量机和逻辑回归3种机器学习方法构建宫颈癌根治性切除术后尿潴留风险预测模型,采用准确性、召回率、精确率、F1指数和受试者工作特征(ROC)曲线下面积(AUC)评价模型性能.结果:共纳入病人的年龄、疾病分期、体质指数等8个变量.选择80%的数据…查看全部>>
Objective:To establish risk prediction model of postoperative urinary retention using decision tree,logistic regression and support vector machine and compare their performance,in order to provide references for evaluating and preventing urinary retention after radical resection.Methods:The medical history information of 459 patients who underwent radical resection in Shanghai First Maternal and Infant Health Hospital from 2018 to 2021 was collected retrospe…查看全部>>
陆宇;江会
同济大学附属妇产科医院,上海 200040同济大学附属妇产科医院,上海 200040
宫颈癌尿潴留危险因素机器学习预测模型决策树支持向量机逻辑回归
cervical cancerurinary retentionrisk factorsmachine learningprediction modeldecision treesupport vector machineLogistic regression
《护理研究》 2024 (1)
24-30,7
上海申康医院发展中心管理研究项目,编号:2022SKMR-18
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