诊断急性尾炎的另一种方法:机器学习
Ahmet Tarik Harmantepe1, Enis Dikicier2, Emre Gönüllü1
1Sakarya University Education and Research Hospital, Department of General Surgery.
Polski przeglad chirurgiczny
|April 17, 2024
概括
机器学习可以有效地预测急性尾炎,这是一个常见的外科紧急情况. 这项研究展示了一种使用算法和患者数据的实用,快速和廉价的诊断方法.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 手术诊断手术诊断的使用方法
背景情况:
- 急性尾炎是紧急手术的最常见原因.
- 准确和及时的诊断对于有效的治疗至关重要.
- 现有的诊断方法可能是侵入性的或昂贵的.
研究的目的:
- 开发和评估用于预测急性尾炎的机器学习模型.
- 建立一种简单,廉价,快速的尾炎诊断方法.
- 利用患者数据和算法来提高诊断准确度.
主要方法:
- 对怀疑急性尾炎的手术治疗患者 (2011-2021) 的分析.
- 包括患有右下象限疼痛的患者.
- 使用机器学习算法利用性别和血液图数据 (Python 3.7).
主要成果:
- 结合后勤回归,k-最近邻居,支持向量机器和神经网络的投票分类器实现了86.2%的准确性.
- 投票分类器显示了83.7%的敏感度和88.6%的特异性.
- 各个算法的准确性各不相同,神经网络的准确率为83.9%,物流回归率为82.7%.
结论:
- 机器学习为诊断急性尾炎提供了一种有效的方法.
- 开发的方法是实用的,简单的,快速的,并且具有成本效益.
- 这种人工智能驱动的方法可以帮助在急性尾炎的早期和准确的预测.
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