Predicting post-treatment survivability of patients with breast cancer using Artificial Neural Network methods

In the last decade, the use of data mining techniques has become widely accepted in medical applications, especially in predicting cancer patients' survival. In this study, we attempted to train an Artificial Neural Network (ANN) to predict the patients' five-year survivability. Breast cancer patients who were diagnosed and received standard treatment in one hospital during 2000 to 2003 in Taiwan were collected for train and test the ANN. There were 604 patients in this dataset excluding died not in breast cancer. Among them 140 patients died within five years after their first radiotherapy treatment. The artificial neural networks were created by STATISTICA
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software. Five variables (age, surgery and radiotherapy type, tumor size, regional lymph nodes, distant metastasis) were selected as the input features for ANN to predict the five-year survivability of breast cancer patients. We trained 100 artificial neural networks and chose the best one to analyze. The accuracy rate is 85% and area under the receiver operating characteristic (ROC) curve is 0.79. It shows that artificial neural network is a good tool to predict the five-year survivability of breast cancer patients.