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在横向头脑测量测量中使用X指数和C4.5算法来识别面图案

Merve Gonca¹, Mehmet Birol Özel²

¹Faculty of Dentistry, Department of Orthodontics, Recep Tayyip Erdoğan University, Rize, Turkey.

BMC oral health

|July 27, 2025

概括

机器学习确定了四种面子表型,揭示了不同的骨和牙特征. 部平面倾斜和间角是关键的差异化因素,改善了正牙诊断和治疗规划.

科学领域:

矯正牙科矯正牙科是一種矯正牙科.
面生物学面生物学
医学中的数据科学.

背景情况:

面表型是个性化的正牙诊断和治疗规划的关键.
传统的骨分类可能过于简化了复杂的缺陷.
机器学习为细微的亚表型分类提供了潜力.

研究的目的:

使用无监督机器学习来分类面子类型.
为了确定区分这些子表型的关键头度变量.
为了提高诊断准确度在牙科.

主要方法:

应用X-平均集群到330个治疗前侧向脑图 (LCR).
利用C4.5决策树来识别有歧视性的脑力测量变量.
分析了斜腰和垂直的骨和牙特征.

主要成果:

确定了四种不同的面子表型.
个性化ANB (°) 是主要的根节点区分器.
间角和N-Go-Gn (°) 对于区分集群至关重要.
在大多数头脑测量测量中观察到显著的差异.

结论:

通过X-means聚类和C4.5决策树,有效地确定了四种面子表型.
部平面倾斜度和间角是关键的歧视因素.
这种方法增强了对骨缺陷相互作用的理解,以改善临床结果.

关键词:

算法算法是一种算法.头脑测量是一项头脑测量.机器学习是机器学习.矯正牙科矯正牙科是一種矯正牙科.

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