Version 1
: Received: 22 June 2020 / Approved: 24 June 2020 / Online: 24 June 2020 (18:02:03 CEST)
How to cite:
Bandyopadhyay, S.; DUTTA, S. Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN. Preprints2020, 2020060297. https://doi.org/10.20944/preprints202006.0297.v1.
Bandyopadhyay, S.; DUTTA, S. Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN. Preprints 2020, 2020060297. https://doi.org/10.20944/preprints202006.0297.v1.
Cite as:
Bandyopadhyay, S.; DUTTA, S. Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN. Preprints2020, 2020060297. https://doi.org/10.20944/preprints202006.0297.v1.
Bandyopadhyay, S.; DUTTA, S. Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN. Preprints 2020, 2020060297. https://doi.org/10.20944/preprints202006.0297.v1.
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.
Keywords
breast cancer; predictive model; stacked GRU-LSTM-BRNN; LSTM; GRU; RNN
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
