以類神經網路及分類迴歸樹輔助肝癌病患預測存活情形

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