Hearing Recovery Prediction for Patients with Chronic Otitis Media Who Underwent Canal-Wall-Down Mastoidectomy

被引:0
|
作者
Chae, Minsu [1 ]
Yoon, Heesoo [2 ]
Lee, Hwamin [1 ]
Choi, June [1 ,2 ]
机构
[1] Korea Univ, Dept Biomed Informat, Coll Med, Seoul 02841, South Korea
[2] Korea Univ, Dept Otorhinolaryngol Head Head & Neck Surg, Ansan Hosp, Ansan 15355, South Korea
关键词
chronic otitis media; hearing recovery; machine learning; canal-wall-down mastoidectomy; QUALITY-OF-LIFE; OUTCOMES; EAR;
D O I
10.3390/jcm13061557
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Chronic otitis media affects approximately 2% of the global population, causing significant hearing loss and diminishing the quality of life. However, there is a lack of studies focusing on outcome prediction for otitis media patients undergoing canal-wall-down mastoidectomy. Methods: This study proposes a recovery prediction model for chronic otitis media patients undergoing canal-wall-down mastoidectomy, utilizing data from 298 patients treated at Korea University Ansan Hospital between March 2007 and August 2020. Various machine learning techniques, including logistic regression, decision tree, random forest, support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (light GBM), were employed. Results: The light GBM model achieved a predictive value (PPV) of 0.6945, the decision tree algorithm showed a sensitivity of 0.7574 and an F1 score of 0.6751, and the light GBM algorithm demonstrated the highest AUC-ROC values of 0.7749 for each model. XGBoost had the most efficient PR-AUC curve, with a value of 0.7196. Conclusions: This study presents the first predictive model for chronic otitis media patients undergoing canal-wall-down mastoidectomy. The findings underscore the potential of machine learning techniques in predicting hearing recovery outcomes in this population, offering valuable insights for personalized treatment strategies and improving patient care.
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页数:14
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