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The Application of Data Mining Techniques to Oral Cancer Prognosis

  • Wan-Ting Tseng
  • Wei-Fan Chiang
  • Shyun-Yeu Liu
  • Jinsheng Roan
  • Chun-Nan Lin
Transactional Processing Systems
Part of the following topical collections:
  1. Transactional Processing Systems

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.

Keywords

Oral cancer Survival analysis Data mining Cluster analysis 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Wan-Ting Tseng
    • 1
  • Wei-Fan Chiang
    • 1
  • Shyun-Yeu Liu
    • 1
  • Jinsheng Roan
    • 2
  • Chun-Nan Lin
    • 3
  1. 1.Department of Oral Maxillofacial SurgeryChi-Mei Medical CenterTainan CityTaiwan Republic of China
  2. 2.Department of Information ManagementNational Chung Cheng UniversityChia-yi CountyTaiwan Republic of China
  3. 3.Department of Logistics ManagementShu-Te UniversityKaohsiung CityTaiwan Republic of China

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