基于特征选择和XGBoost优化的术中低体温预测

Intraoperative Hypothermia Prediction Model Based on Feature Selection and XGBoost Optimization

针对全麻手术患者术中低体温发生率高、影响因素复杂的问题,提出了一种基于特征选择和XGBoost优化的术中低体温预测模型,以更好辅助医生对全麻手术患者的临床诊断。首先,利用随机森林(Random forest,RF)在处理高维数据集上的优势,通过RF的袋外估计法进行特征选择。然后,以极端梯度提升(XGBoost)为基础,利用基于精英保留策略的遗传算法,即EGA算法优化XGBoost超参数。最后,根据最优参数训练预测模型,并用于术中低体温预测。该模型组合利用3种算法优点,提升模型泛化能力和预测精度。实验结果表明:本文所提模型与逻辑回归、支持向量机等7种机器学习预测模型相比,预测准确率、精确度、召回率、AUC均有优势;与现有其他预测模型相比均有提升。

In view of the high incidence of intraoperative hypothermia and complex influencing factors in patients undergoing anesthesia,a prediction model of intraoperative hypothermia based on feature selection and XGBoost optimization is proposed to better assist doctors in the clinical diagnosis of patients.Firstly,the random forest(RF)is used to deal with the high-dimensional data sets,and features are selected by the RF out-of-bag estimation.Then,XGBoost hyperparameters are optimized using the genetic algorithm based on elite retention strategy,i.e.,EGA.Finally,the prediction is trained according to the optimal parameters and thus can be used to predict intraoperative hypothermia.This model combines the advantages of three algorithms to improve model generalization ability and prediction accuracy.The experimental result shows that the proposed model performs better other seven machine learning classification prediction models such as logistic regression,support vector machine,and so on in prediction accuracy,precision,recall and AUC,and overcomes the three representative hyperparameter tuning methods.