<strong>Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN</strong>

Abstract

Breast Cancer diagnosis is one of the most studied problems in the medical domain. In the medical domain, cancer diagnosis has been studied extensively which instantiates the need of early prediction of cancer disease. For obtaining advance prediction, health records are exploited and given as input to an automated system. This paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining possibility of being affected by breast cancer. Proposed model is compared against other baseline classifiers such as stacked Simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that stacked GRU-LSTM-BRNN yield better classification performance for predictions related to breast cancer disease.

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

“…Breast cancer diagnosis is a challenge task till today and emphasizes the need for early disease prediction. Deep learningbased recurrent neural network model GRU-LSTM-BRNN, predict breast cancer using patient health records [5]. Classifiers with five different feature selection are used to identify relevant features from microarray cancer datasets [6].…”

“…Equation ( 5) displays the mathematical equation for the forget gate. f_n = σ(W_f * [g_(n-1), x_n] + b_f) (5) Output Gate (o_n), updates the cell state, output gate selects the LSTM cell output. Based on the cell state and the current input (x_n), calculates the new hidden state (g_n).…”