Multi-criterion mammographic risk analysis supported with multi-label fuzzy-rough feature selection.

Journal: Artificial intelligence in medicine
Published Date: Sep 25, 2019

Abstract

CONTEXT AND BACKGROUND: Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution.

Authors

  • Yanpeng Qu
    Information Technology College, Dalian Maritime University, Dalian 116026, China; Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3DB, UK. Electronic address: yanpengqu@dlmu.edu.cn.
  • Guanli Yue
    Information Technology College, Dalian Maritime University, Dalian 116026, China.
  • Changjing Shang
    Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3DB, UK.
  • Longzhi Yang
    Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
  • Reyer Zwiggelaar
    Department of Computer Science, Aberystwyth University, Ceredigion, United Kingdom.
  • Qiang Shen
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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