Background: Breast cancer is the most common type of cancer amongst women worldwide. Considering its high incidence, effective detection and prognosis of this type of cancer may have a significant effect on reducing expenditures. In this study, we propose a model to predict the 60-month survivability in patients with breast cancer and investigate the effects of each feature on the obtained model.
Methods: We base this model on the information gathered by the Breast Disease Research Center, Shiraz University of Medical Sciences, Shiraz, Iran from 5673 patients with breast cancer. The goal of this study was to predict breast cancer survivability at early diagnosis, so the features used in the research are among those considered affordable, specifically at the initial steps of diagnosis. After preprocessing all of the cases and features, we constructed this model based on 1930 cases and 16 of their associated features using logistic regression method. The model then was evaluated with 10-fold cross validation.
Results: Based on all subsets of the 16 features, we evaluated numerous models. We selected a model that achieved the best sensitivity and specificity, and used fewer features as the best model. We considered this model for further analysis, which is consisted of following features: age at the time of diagnosis, type of invasion, HER2, size of the tumor, in situ component, lymph node involvement ratio, progesterone receptor status, and the total number of dissected lymph nodes. The best model obtained overall accuracy, specificity and sensitivity of 72.49%, 72.83%, and 71.85%, respectively.
Conclusion: The performance of model is quite satisfactory due to the fact that we only used features, which could be obtained at the initial steps of diagnosis. Even though, the effect of patient’s age is controversial, we concluded that ageing would decrease the 60-month survivability. Our model indicated that having all type of invasions (i.e. vascular, lymphatic, etc.) would result in poorer chance of survival compared to other features effect.