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International Conference on Cyberspace Data and Intelligence

International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health

CyberDI 2019, CyberLife 2019: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health pp 417–430Cite as

Breast Cancer Risk Assessment Model Based on sl-SDAE

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1138))

Abstract

In recent years, the incidence of breast cancer among women in China has increased year by year and it has become the most common malignant tumor in women in China. There are already breast cancer risk assessment models for women in Europe and the United States. However, there is no effective breast cancer risk assessment model suitable for women in China. The paper established an effective breast cancer risk assessment model. It selected the survey data of breast cancer population in China as a data set. The paper combines SDAE and LSTM to build a model based on deep learning methods. It uses the roc curve as an indicator of the experimental results. Experiments show that the model has better performance than traditional machine learning algorithms.

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Acknowledgement

This study was supported by State’s Key Project of Research and Development Plan (No. 2018YFC0810601, No. 2016YFC0901303). The work was conducted at University of Science and Technology Beijing.

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Authors and Affiliations

  1. University of Science and Technology Beijing, Beijing, 100083, China

    Xueni Li & Zhiguo Shi

Authors
  1. Xueni Li
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  2. Zhiguo Shi
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Corresponding author

Correspondence to Zhiguo Shi .

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Editors and Affiliations

  1. University of Science and Technology, Beijing, China

    Huansheng Ning

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Li, X., Shi, Z. (2019). Breast Cancer Risk Assessment Model Based on sl-SDAE. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_30

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  • DOI: https://doi.org/10.1007/978-981-15-1925-3_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1924-6

  • Online ISBN: 978-981-15-1925-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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