Breast Cancer Prediction based on Deep Neural Network Model Implemented AWS Machine Learning Platform
L. D. P Cuong1, Wang Dong2, D. T. Hoang3, L. M. N Uyen4
1L. D. P Cuong*, College of Electrical and Information Engineering, Hunan University, Changsha, China.
2Wang Dong, College of Electrical and Information Engineering, Hunan University, Changsha, China.
3D. T. Hoang, College of Electrical and Information Engineering, Hunan University, Changsha 410082, China.
4L. M. N Uyen, College of Life science, Hunan Normal University, Changsha, China.
Manuscript received on June 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 868-873 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3944079220/2020©BEIESP | DOI: 10.35940/ijrte.B3944.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer in women is one of the most dangerous cancers leading to death in women by developing breast tissue. In this work, the application of the Deep Neural Network (DNN) model is implemented on AWS machine learning platform, besides, a comparison with other ML techniques includes XG Boost and Random Forest on a public dataset. Breast cancer prediction based on DNN model with Hyperparameter tuning has the best results of the plot of model accuracy for the training and validation sets and performance evaluation metrics to test the model.
Keywords: Breast cancer, Deep Neural Network, Deep Learning, AWS Sage Maker, Docker containers.