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

Cheng Mei Chen, Chien-Yeh Hsu, Hung Wen Chiu, Hsiao Hsien Rau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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#60;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.

Original languageEnglish
Title of host publicationProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Pages811-815
Number of pages5
Volume2
DOIs
Publication statusPublished - 2011
Event2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
Duration: Jul 26 2011Jul 28 2011

Other

Other2011 7th International Conference on Natural Computation, ICNC 2011
CountryChina
CityShanghai
Period7/26/117/28/11

Keywords

  • artificial neural networks
  • classification and regression trees
  • liver cancer
  • prediction model

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Neuroscience(all)

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