By Topic

Prediction of survival in patients with liver cancer using artificial neural networks and classification and regression trees

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Cheng-Mei Chen ; Grad. Inst. of Med. Inf., Taipei Med. Univ., Taipei, Taiwan ; Chien-Yeh Hsu ; Hung-Wen Chiu ; Hsiao-Hsien Rau

This study established a survival prediction model for liver cancer using data mining technology. The data were collected from the cancer registration database of a medical center in Northern Taiwan between 2004 and 2008. A total of 227 patients were newly diagnosed with liver cancer during this time. With literature review, and expert consultation, nine variables pertaining to liver cancer survival were analyzed using t-test and chi-square test. Six variables showed significant. Artificial neural network (ANN) and classification and regression tree (CART) were adopted as prediction models. The models were tested in three conditions; one variable (clinical stage alone), six significant variables, and all nine variables (significant and non significant). 5-year survival was the output prediction. The results showed that the ANN model with nine input variables was superior predictor of survival (p<;0.001). The area under receiver operating characteristic curve (AUC) was 0.915, 0.87, 0.88, and 0.87 for accuracy, sensitivity, and specificity respectively. The ANN model is significant more accurate than CART model when predict survival for liver cancer and provide patients information for understanding the treatment outcomes.

Published in:

Natural Computation (ICNC), 2011 Seventh International Conference on  (Volume:2 )

Date of Conference:

26-28 July 2011