南方医科大学学报2025，Vol.45Issue(9)：2019-2025,7.DOI:10.12122/j.issn.1673-4254.2025.09.21
经尿道前列腺钬激光剜除术后低体温风险预测模型:基于逻辑回归、决策树和支持向量机
Construction of risk prediction models of hypothermia after transurethral holmium laser enucleation of the prostate based on three machine learning algorithms
摘要
Abstract
Objective To develop risk prediction models for postoperative hypothermia after transurethral holmium laser enucleation of the prostate(HoLEP)using machine learning algorithms.Methods We retrospectively analyzed the clinical data of 403 patients from our center(283 patients in the training set and 120in the internal validation set)and 120 patients from Weifang People's Hospital(as the external validation set).The risk prediction models were built using logistic regression,decision tree and support vector machine(SVM),and model performance was evaluated in terms of accuracy,recall,precision,F1 score and AUC.Results Operation duration,prostate weight,intraoperative irrigation volume,and being underweight were identified as the predictors of postoperative hypothermia following HoLEP.Among the 3 algorithms,SVM showed the best precision rate and accuracy in all the 3 data sets and the best area under the ROC(AUC)in the training set and validation set,followed by logistic regression,which had a similar AUC in the two data sets.SVM outperformed logistic regression and decision tree models in the validation set in precision,accuracy,recall,F1 score,and AUC,and performed well in the external validation set with better precision rate and accuracy than logistic regression and decision tree models but slightly lower recall rate,F1 index,and AUC value than the decision tree model.SVM outperformed logistic regression and decision tree models in precision,accuracy,F1 score,and AUC in the training set,but had slightly lower recall rate than the decision tree.Conclusion Among the 3 models,SVM has the best performance and generalizability for predicting post-HoLEP hypothermia risk to provide support for clinical decisions.关键词
前列腺/低体温/危险因素/机器学习/预测模型Key words
prostate/hypothermia/risk factors/machine learning/prediction model引用本文复制引用
姜君,封硕,孙银贵,安燕..经尿道前列腺钬激光剜除术后低体温风险预测模型:基于逻辑回归、决策树和支持向量机[J].南方医科大学学报,2025,45(9):2019-2025,7.基金项目
山东省中医药科技项目(Q-2023147) （Q-2023147）
潍坊市科学技术发展计划(医学类)(2023YX057) （医学类）
潍坊市卫健委科研项目(WFWSJK-2023-033) （WFWSJK-2023-033）
潍坊医学院2022年校级教育教学改革与研究课题(2022YB051) （2022YB051）
