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
| Translated title of the contribution | Prediction of survival in patients with liver cancer using artificial neural networks and classification and regression trees |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 481-493 |
| Number of pages | 13 |
| Journal | Taiwan Journal of Public Health |
| Volume | 30 |
| Issue number | 5 |
| Publication status | Published - Oct 2011 |
Keywords
- Artificial neural networks
- Classification and regression trees
- Liver cancer
- Prediction model
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
