Breast Cancer Disease Prediction With Recurrent Neural Networks (RNN)

Abstract

Cancer is a collaborative amalgamation of diseases that involves abnormal increase in cell growth with the potential of occupying and attacking the entire body. According to studies, breast cancer most likely occurs in women and it has become the second biggest cause of female death. Due to its widespread penetration and significance, many researchers have analyzed the phenomenon and further studies are still required to reach an optimum outcome. This study applies deep learning technique in conjunction with Recurrent Neural Networks (RNN) to predict the formation of breast cancer disease so that doctors will perform the diagnosis more properly. To assess the efficiency of the proposed method, breast cancer data belonging to UC Irvine repository were used. Precision, recall, accuracy, and f1 score of the proposed method showed good scores and the proposed technique performed well.