Construction of risk prediction models of hypothermia after transurethral holmium laser enucleation of the prostate based on three machine learning algorithms.
10.12122/j.issn.1673-4254.2025.09.21
- Author:
Jun JIANG
1
;
Shuo FENG
2
;
Yingui SUN
3
;
Yan AN
3
Author Information
1. Operating Room, Affiliated Hospital of Shandong Second Medical University, Weifang 261000, China.
2. Department of Gynecology, Affiliated Hospital of Shandong Second Medical University, Weifang 261000, China.
3. Department of Anesthesiology, Affiliated Hospital of Shandong Second Medical University, Weifang 261000, China.
- Publication Type:Journal Article
- Keywords:
hypothermia;
machine learning;
prediction model;
prostate;
risk factors
- MeSH:
Humans;
Male;
Retrospective Studies;
Machine Learning;
Transurethral Resection of Prostate/adverse effects*;
Hypothermia/etiology*;
Prostatic Hyperplasia/surgery*;
Algorithms;
Lasers, Solid-State;
Risk Assessment;
Postoperative Complications;
Decision Trees;
Logistic Models;
Aged;
Middle Aged;
Support Vector Machine
- From:
Journal of Southern Medical University
2025;45(9):2019-2025
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES: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.
CONCLUSIONS:Among the 3 models, SVM has the best performance and generalizability for predicting post-HoLEP hypothermia risk to provide support for clinical decisions.
