Breast cancer recurrence prediction using random forest model

Abstract

Breast cancer is the second most common cause of death among Australian females. To reduce the probability of death, early detection and prevention of breast cancer is a crucial factor. Evaluating the probability of breast cancer recurrence is an important act related to breast cancer prognosis. The aim of this paper is to predict the probability of breast cancer recurrence among patients. The researchers individually applied Random Forest and Deep Neural Network classifiers to increase the prediction accuracy of those models. Wisconsin Prognosis Breast Cancer dataset was obtained from UCI machine learning Repository. The results of our experiment indicate that Random Forest technique achieved the highest accuracy compared to the existing works.