title: The application of data mining techniques to oral cancer prognosis.
abstract: 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.
authors: Department of Oral Maxillofacial Surgery, Chi-Mei Medical Center, No.201, Taikang, Taikang Vil., Liuying Dist., Tainan City, Taiwan Republic of China.
authors: Wan-Ting
authors: WT
authors: Tseng
authors: Wei-Fan
authors: WF
authors: Chiang
authors: Shyun-Yeu
authors: SY
authors: Liu
authors: Jinsheng
authors: J
authors: Roan
authors: Chun-Nan
authors: CN
authors: Lin
mesh_terms: D000367:Age Factors; D000428:Alcohol Drinking / Q000453:epidemiology; D057225:Data Mining / Q000379:methods*; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models; D009062:Mouth Neoplasms / Q000401:mortality* / Q000473:pathology; D016571:Neural Networks, Computer; D012737:Sex Factors; D012907:Smoking / Q000453:epidemiology; D016019:Survival Analysis