A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features.

Journal: BMC bioinformatics
Published Date: Jun 10, 2019

Abstract

BACKGROUND: It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time.

Authors

  • Hui Huang
    Department of Biobank, The Sixth Affiliated People's Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Xi'an Feng
    School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, 710072, China.
  • Suying Zhou
    Pathology Department of Wenzhou People's Hospital, Wenzhou, 325035, China.
  • Jionghui Jiang
    Zhijiang College of Zhejiang University of Technology, Hangzhou, 310024, China.
  • Huiling Chen
    College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
  • Yuping Li
    Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
  • Chengye Li
    Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. lichengye41@126.com.

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