Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN

Dutta, Shawni; Mandal, Jyotsna Kumar; Kim, Tai Hoon; Bandyopadhyay, Samir Kumar

doi:10.2478/acss-2020-0018

Abstract

Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease. To obtain advance prediction, health records are exploited and given as input to an automated system. The 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 the possibility of being affected by breast cancer. The 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 the stacked GRU-LSTM-BRNN model yields better classification performance for predictions related to breast cancer disease.

References

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Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN

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