Department of Oral Maxillofacial Surgery, Chi-Mei Medical Center, No.201, Taikang, Taikang Vil., Liuying Dist., Tainan City, Taiwan Republic of China.
J Med Syst. 2015 May;39(5):59. doi: 10.1007/s10916-015-0241-3. Epub 2015 Mar 22.
This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.
本研究采用了一种综合程序，结合数据挖掘技术的聚类和分类特征，以确定过去口腔癌患者死亡或存活病例中表现出的症状差异。使用了两种数据挖掘工具，即决策树和人工神经网络，来分析口腔癌的历史病例，并将其性能与常用的统计分析工具逻辑回归进行了比较。决策树和人工神经网络模型均显示出优于传统统计模型的性能。然而，对于临床医生而言，决策树模型创建的树相对比人工神经网络模型更容易解释。聚类分析还发现，那些处于第 4 期且具有以下四个特征的患者的生存率极低：pN 为 N2b，RLNM 水平为 I-III 级，AJCC-T 为 T4，以及细胞突变情况（G）为中度。
