以類神經網路及分類迴歸樹輔助肝癌病患預測存活情形

Translated title of the contribution: Prediction of survival in patients with liver cancer using artificial neural networks and classification and regression trees

G. Mei Chen, Chien-Yeh Hsu, Hung Wen Chiu, B. A I Chyi-Huey, W. U. Po-Hsun

Research output: Contribution to journalArticle

Abstract

Objectives: This study created a survival prediction model for liver cancer using data mining algorithms. Methods: The data were collected from the cancer registry 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. Following a literature review, expert consultation, and collection of patients' data, nine variables pertaining to liver cancer survival rates were analyzed using t-tests and chi-square tests. Six variables were significant. An artificial neural network (ANN) and a classification and regression tree (CART) algorithm 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). Five-year survival was the output prediction. Results: The ANN model with nine input variables was a superior predictor of survival (p

Original languageTraditional Chinese
Pages (from-to)481-493
Number of pages13
JournalTaiwan Journal of Public Health
Volume30
Issue number5
Publication statusPublished - Oct 2011

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Liver Neoplasms
Survival
Neural Networks (Computer)
Data Mining
Chi-Square Distribution
Taiwan
Registries
Referral and Consultation
Survival Rate
Neoplasms

Keywords

  • Artificial neural networks
  • Classification and regression trees
  • Liver cancer
  • Prediction model

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

以類神經網路及分類迴歸樹輔助肝癌病患預測存活情形. / Chen, G. Mei; Hsu, Chien-Yeh; Chiu, Hung Wen; Chyi-Huey, B. A I; Po-Hsun, W. U.

In: Taiwan Journal of Public Health, Vol. 30, No. 5, 10.2011, p. 481-493.

Research output: Contribution to journalArticle

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