An Improved Strategy for Predicting Diagnosis, Survivability, and Recurrence of Breast Cancer
Souad Larabi-Marie-Sainte, Tanzila Saba, Deem Alsaleh, Mashael Bin Alamir Alotaibi · Journal of Computational and Theoretical Nanoscience · 2019
Breast Cancer is a common disease among females. Early detection of the Breast Cancer aids in an easier efficient treatment. The application of Machine Learning algorithms can help in the diagnosis of this disease. There are three main problems related to Breast Cancer. The existing works focused only on one problem. In addition, the resulted accuracy still needs improvement. This research paper aims to identify the Breast Cancer diagnosis, predict the recurrence of the disease, and predict the survivability of its patients. This is achieved by using the Feedforward Neural Network (FFN) on the SEER (Surveillance, Epidemiology, and End Results) dataset by using different attributes and preprocessing of data for each problem. The obtained FFN classification accuracy resulted in 99.8% for the Breast Cancer diagnosis, 88.1% for the Breast Cancer recurrence, and 97.3% for the survivability.