Prediction of breast cancer survival through knowledge discovery in databases

The collection of large volumes of medical data has offered an opportunity to develop prediction models for
survival by the medical research community. Medical researchers who seek to discover and extract hidden
patterns and relationships among large number of variables use knowledge discovery in databases (KDD) to
predict the outcome of a disease. The study was conducted to develop predictive models and discover
relationships between certain predictor variables and survival in the context of breast cancer. This study is Cross
sectional. After data preparation, data of 22,763 female patients, mean age 59.4 years, stored in the Surveillance
Epidemiology and End Results (SEER) breast cancer dataset were analyzed anonymously. IBM SPSS Statistics
16, Access 2003 and Excel 2003 were used in the data preparation and IBM SPSS Modeler 14.2 was used in the
model design. Support Vector Machine (SVM) model outperformed other models in the prediction of breast
cancer survival. Analysis showed SVM model detected ten important predictor variables contributing mostly to
prediction of breast cancer survival. Among important variables, behavior of tumor as the most important
variable and stage of malignancy as the least important variable were identified. In current study, applying of the
knowledge discovery method in the breast cancer dataset predicted the survival condition of breast cancer
patients with high confidence and identified the most important variables participating in breast cancer survival.