通过机器学习预测噪音引起的听力损失:耳作为预测因素的影响
Emre Soylemez1,2, Isa Avci3, Elif Yildirim3
1Department of Audiometry, Vocational School of Health Services, Karabuk University, Karabuk, Türkiye.
The Journal of laryngology and otology
|May 8, 2024
概括
这项研究发现,支向量机器模型准确地预测了噪音引起的听力损失,耳是关键指标. 纳入耳声可以增强职业健康和安全计划.
科学领域:
- 职业健康 职业健康 职业健康
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 噪音引起的听力损失 (NIHL) 是一个重要的职业危险,特别是在金属制造业等行业.
- 早期检测和预防对于减轻对工人的长期健康影响至关重要.
- 耳是与听力损伤相关的常见症状,但其对NIHL的预测价值需要进一步调查.
研究的目的:
- 确定最有效的机器学习模型来预测NIHL.
- 评估 tinnitus 对这些预测模型准确性的影响.
- 为改进职业健康战略的制定提供信息.
主要方法:
- 一组200名金属工业工人接受了纯音调听力测量.
- 职业暴露史被收集并编译成一个数据集.
- 在80%的数据上训练了6个机器学习模型,在剩余20%的数据上进行了验证.
主要成果:
- 59.5%的工人表现出某种程度的听力损失.
- 年龄和耳被确定为NIHL的重要预测因素,耳是第二个最重要的因素.
- 支持矢量机器模型实现了最高的性能,准确率为90%,F1得分为91%,精度为95%,回忆率为88%.
结论:
- 支持矢量机在预测NIHL方面表现出高准确度.
- 将耳作为预测模型中的风险因素可以显著提高其有效性.
- 这种方法可以提高NIHL预防的职业健康和安全倡议的成功率.
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