用机器学习预测噪声性听力损失：耳鸣作为预测因素的影响。
Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor.
机构信息
Department of Audiometry, Vocational School of Health Services, Karabuk University, Karabuk, Türkiye.
Audiology and Speech Pathology Ph.D. Program, Health Sciences Institute, Ankara University, Ankara, Türkiye.
出版信息
J Laryngol Otol. 2024 Oct;138(10):1030-1035. doi: 10.1017/S002221512400094X. Epub 2024 May 9.
OBJECTIVES
This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models' accuracy.
METHODS
Two hundred workers employed in a metal industry were selected for this study and tested using pure tone audiometry. Their occupational exposure histories were collected, analysed and used to create a dataset. Eighty per cent of the data collected was used to train six machine learning models and the remaining 20 per cent was used to test the models.
RESULTS
Eight workers (40.5 per cent) had bilaterally normal hearing and 119 (59.5 per cent) had hearing loss. Tinnitus was the second most important indicator after age for noise-induced hearing loss. The support vector machine was the best-performing algorithm, with 90 per cent accuracy, 91 per cent F1 score, 95 per cent precision and 88 per cent recall.
CONCLUSION
The use of tinnitus as a risk factor in the support vector machine model may increase the success of occupational health and safety programmes.
目的
本研究旨在确定哪种机器学习模型最适合预测噪声性听力损失以及耳鸣对模型准确性的影响。
方法
本研究选择了 200 名在金属行业工作的工人进行纯音测听测试。收集、分析他们的职业暴露史，并用于创建数据集。所收集数据的 80%用于训练六个机器学习模型，其余 20%用于测试模型。
结果
8 名工人（40.5%）双侧听力正常，119 名工人（59.5%）有听力损失。耳鸣是继年龄之后导致噪声性听力损失的第二重要指标。支持向量机是表现最好的算法，准确率为 90%，F1 得分为 91%，精度为 95%，召回率为 88%。
结论
在支持向量机模型中使用耳鸣作为风险因素，可能会提高职业健康和安全计划的成功率。
Zotero 插件