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集群和机器学习模型的骨类I和II参数的阿拉伯正牙患者

Kareem Midlej¹, Osayd Zohud¹, Iqbal M Lone¹

¹Department of Clinical Microbiology and Immunology, Faculty of Medicine and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.

Journal of clinical medicine

|February 13, 2025

概括

在阿拉伯患者中,新的机器学习模型准确地分类了骨I类闭塞 (SCIO) 和骨II类缺陷闭塞 (SCIIMO). 这可以提高诊断准确度,为正牙治疗规划提供帮助.

科学领域:

矯正牙科矯正牙科是一種矯正牙科.
牙科诊断牙科诊断牙科诊断
医疗保健中的机器学习

背景情况:

ортодонтических问题影响至关重要的功能,如吞,言语,和美学.
准确的诊断和治疗对于患有缺陷的患者至关重要.
这项研究的重点是对阿拉伯人群中的骨I类闭塞 (SCIO) 和骨II类缺陷闭塞 (SCIIMO) 的分类.

研究的目的:

为SCIO和SCIIMO引入新的分类方法.
通过使用等级聚类来识别缺陷类中的趋势.
应用机器学习 (ML) 模型来预测分类结果.

主要方法:

对394名被诊断患有SCIO/SCIIMO的阿拉伯患者的横向头脑测量参数的分析.
应用等级聚类来识别不同的患者子组.
开发和评估ML模型,包括随机森林和分类和回归树,用于分类准确性.

主要成果:

层次分类显示了SCIO和SCIIMO的三个不同的集群,突出了下逆转和特定脑角的变化.
机器学习模型实现了高分类准确性:使用所有测量结果的随机森林和CART实现了0.87.
在ML中仅使用ANB角度和Wits评估,准确度为0.78.

结论:

聚类分析在SCIO和SCIIMO中发现了独特的模式,影响了诊断和治疗计划.
开发的ML模型在诊断SCIO/SCIIMO患者方面表现出高准确度.
这种方法有望提高牙矯正的诊断精度.

关键词:

头脑测量参数的头脑测量参数疾病分类疾病分类.机器学习是机器学习.骨的变形骨的变形骨缺陷是指骨的缺陷.

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