International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
line decor
  
line decor

 Volume 5, Issue 11 (November 2018), Pages: 91-94

----------------------------------------------

 Technical Note

 Title: Predicting the predictor's variables of survival time for oral cancer with decision tree analysis

 Author(s): Wan Muhamad Amir W Ahmad 1, *, Mohamad Shafiq Mohd Ibrahim 1, Rabiatul Adawiyah Rohim 1, Zalila Ali 2

 Affiliation(s):

 1School of Dental Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
 2School of Mathematics Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia

 https://doi.org/10.21833/ijaas.2018.11.013

 Full Text - PDF          XML

 Abstract:

Decision tree analysis is one of the famous analyses which assist the researcher to identify the associated factor that contributes to the certainty factor. In this study, we proposed a decision tree model with the high-risk factor and try to estimates the importance of every each predictor. The result reveals that the survival time for the patient is mostly depending on nerve invasion and the size of a tumor, followed by alcohol and ethnicity factor.  This promising technique had led to a successful research and give the best results for the decision making especially for the decision maker. In conclusion, this analysis can provide a very useful for forecasting the survival time (in months) of oral cancer patients. 

 © 2018 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Oral cancer, Decision tree, Analysis weight

 Article History: Received 7 June 2018, Received in revised form 10 September 2018, Accepted 15 September 2018

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.11.013

 Citation:

 Ahmad WMAW, Ibrahim MSM, and Rohim RA et al. (2018). Predicting the predictor's variables of survival time for oral cancer with decision tree analysis. International Journal of Advanced and Applied Sciences, 5(11): 91-94

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I11/Ahmad.html

----------------------------------------------

 References (4) 

  1. Mesarić J and Šebalj D (2016). Decision trees for predicting the academic success of students. Croatian Operational Research Review, 7(2): 367-388. https://doi.org/10.17535/crorr.2016.0025   [Google Scholar]
  1. MOH (2007). National cancer registry report Malaysia cancer statistics. Ministry of Health Malaysia, Putrajaya, Malaysia.   [Google Scholar]
  1. Neville BW and Day TA (2002). Oral cancer and precancerous lesions. CA: A Cancer Journal for Clinicians, 52(4): 195-215. https://doi.org/10.3322/canjclin.52.4.195   [Google Scholar]  PMid:12139232
  1. Parkin DM, Bray F, Ferlay J, and Pisani P (2005). Global cancer statistics, 2002. CA: A Cancer Journal for Clinicians, 55(2): 74-108. https://doi.org/10.3322/canjclin.55.2.74   [Google Scholar]  PMid:15761078