School of Industrial Management Engineering, Korea University, Anam-Dong, Seongbuk-Gu, Seoul, Republic of Korea.
PLoS One. 2013 Aug 5;8(8):e67862. doi: 10.1371/journal.pone.0067862. Print 2013.
Relevant statistical modeling and analysis of dental data can improve diagnostic and treatment procedures. The purpose of this study is to demonstrate the use of various data mining algorithms to characterize patients with dentofacial deformities. A total of 72 patients with skeletal malocclusions who had completed orthodontic and orthognathic surgical treatments were examined. Each patient was characterized by 22 measurements related to dentofacial deformities. Clustering analysis and visualization grouped the patients into three different patterns of dentofacial deformities. A feature selection approach based on a false discovery rate was used to identify a subset of 22 measurements important in categorizing these three clusters. Finally, classification was performed to evaluate the quality of the measurements selected by the feature selection approach. The results showed that feature selection improved classification accuracy while simultaneously determining which measurements were relevant.
对牙科数据进行相关的统计建模和分析可以改进诊断和治疗程序。本研究的目的是展示如何使用各种数据挖掘算法来描述牙颌面畸形患者。共检查了 72 名接受过正畸和正颌手术治疗的骨骼畸形患者。每位患者的特征由 22 项与牙颌面畸形相关的测量值来描述。聚类分析和可视化将患者分为三种不同的牙颌面畸形模式。基于错误发现率的特征选择方法用于确定对分类这三个聚类很重要的 22 个测量值的子集。最后，进行分类以评估特征选择方法选择的测量值的质量。结果表明，特征选择提高了分类准确性，同时确定了哪些测量值是相关的。
