以類神經網路及分類迴歸樹輔助肝癌病患預測存活情形

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

研究成果: 雜誌貢獻文章

摘要

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
原文繁體中文
頁(從 - 到)481-493
頁數13
期刊Taiwan Journal of Public Health
30
發行號5
出版狀態已發佈 - 十月 2011

    指紋

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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