中华耳科学杂志2026，Vol.24Issue(8)：779-785,7.DOI:10.3969/j.issn.1672-2922.2026.08.009
基于机器学习与SHAP可解释性的突发性聋预后预测模型构建及验证
Development and validation of machine learning models with SHAP interpretability for prognostic prediction in sudden sensorineural hearing loss
吴珏婷 1陈如如 2章誉耀 1林洪斌 1陈波蓓2
作者信息
- 1. 丽水市人民医院耳鼻咽喉科,浙江 丽水 323000
- 2. 温州医科大学附属第二医院耳鼻咽喉科,浙江 温州 325000
- 折叠
摘要
Abstract
Objective To construct machine learning-based prognostic models for sudden sensorineural hearing loss(SSNHL)to support clinical decision-making.Methods Clinical data from 1120 SSNHL patients(2020～2024)were retrospectively collected from two centers and divided into training(784 cases)and validation sets(336 cases).Key predictors were selected via LASSO regression,and four models-support vector machine(SVM),XGBoost,logistic regression(LR),and random forest(RF)-were developed.Model performance was evaluated using ROC curves,calibration curves,decision curve analysis(DCA),and shapley additive explanations(SHAP).Results Four predictors were identified:audiogram type,age,onset-to-treatment time,and vertigo.In the training set,RF and XGBoost achieved superior AUC(0.804,95%CI:0.774～0.835)compared to SVM(0.722)and LR(0.722)(P＜0.001).Validation set results confirmed robustness(RF AUC=0.773;XGBoost AUC=0.769).SHAP analysis revealed that age＞49.5 years,onset-to-treatment time＞5.5 days,profound hearing loss,and vertigo significantly increased prognostic risk.Calibration and DCA curves demonstrated high consistency between predicted and observed outcomes,with optimal clinical net benefit at 25%～50%threshold probabilities.Conclusions RF and XGBoost models accurately predict SSNHL prognosis,and SHAP-based interpretability enhances clinical utility.关键词
突发性聋/机器学习/预后预测/沙普利可加性解释Key words
sudden sensorineural hearing loss/machine learning/prognostic prediction/shapley additive explanations引用本文复制引用
吴珏婷,陈如如,章誉耀,林洪斌,陈波蓓..基于机器学习与SHAP可解释性的突发性聋预后预测模型构建及验证[J].中华耳科学杂志,2026,24(8):779-785,7.
