目標：使用台灣癌症登記資料，以資料探勘技術，建立肝癌存活的預測模型。方法：收集台灣北部某醫院中心2004至2008年癌症登記資料，新診斷為肝癌的個案共227筆；經文獻查證，專家諮詢及考量病患認知程度，找出9個與肝癌存活有關的變項納入分析，先以T檢定與卡方檢定篩選，有6個達顯著差異，再使用類神經網路與分類迴歸樹演算法，分別以1個(臨床期別)、6個(達顯著差異)、9個(包含無顯著差異)的變數為輸入變項，五年是否存活為輸出變項的方式進行測試。結果：以9個輸入變項建立的類神經網路預測模型成效最佳(p<0.001)，ROC曲線下的面積0.843、準確率0.78、敏感度0.76、特異度0.80。結論：類神經網路所建立肝癌存活的預測模型優於分類迴歸樹。未來在臨床應用上，我們則建議建置「肝癌病患存活預測的資訊系統」，使用9個輸入變項，將類神經網路演算過程電腦化，預測結果以自動化的方式呈現，輔助肝癌病患，了解個人的存活情形與治療成效。

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<0.001). The area under the receiver operating characteristic (ROC) was 0.843, and 0.78, 0.76,and 0.80 for accuracy, sensitivity, and specificity respectively. Conclusions: An artificial neural network was more accurate than a CART system in predicting liver cancer survival. In the future,we suggest developing a computer system using the nine input variables in the ANN prediction
model to predict liver cancer survival. The system would use an ANN algorithm to automatically calculate the prediction result and assist patients in understanding their potential treatment outcomes and survival.