Computational Discrimination of Breast Cancer for Korean Women Based on Epidemiologic Data Only.

Journal: Journal of Korean medical science
Published Date: Jul 15, 2015

Abstract

Breast cancer is the second leading cancer for Korean women and its incidence rate has been increasing annually. If early diagnosis were implemented with epidemiologic data, the women could easily assess breast cancer risk using internet. National Cancer Institute in the United States has released a Web-based Breast Cancer Risk Assessment Tool based on Gail model. However, it is inapplicable directly to Korean women since breast cancer risk is dependent on race. Also, it shows low accuracy (58%-59%). In this study, breast cancer discrimination models for Korean women are developed using only epidemiological case-control data (n = 4,574). The models are configured by different classification techniques: support vector machine, artificial neural network, and Bayesian network. A 1,000-time repeated random sub-sampling validation is performed for diverse parameter conditions, respectively. The performance is evaluated and compared as an area under the receiver operating characteristic curve (AUC). According to age group and classification techniques, AUC, accuracy, sensitivity, specificity, and calculation time of all models were calculated and compared. Although the support vector machine took the longest calculation time, the highest classification performance has been achieved in the case of women older than 50 yr (AUC = 64%). The proposed model is dependent on demographic characteristics, reproductive factors, and lifestyle habits without using any clinical or genetic test. It is expected that the model could be implemented as a web-based discrimination tool for breast cancer. This tool can encourage potential breast cancer prone women to go the hospital for diagnostic tests.

Authors

  • Chiwon Lee
    The Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, Seoul, Korea.
  • Jung Chan Lee
    Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.
  • Boyoung Park
    Graduate School of Cancer Science and Policy and National Cancer Control Institute, National Cancer Center, Goyang, Korea.
  • Jonghee Bae
    Korea Aerospace Research Institute, Dajeon, Korea.
  • Min Hyuk Lim
    Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.
  • Daehee Kang
    Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea. ; Department of Biomedical Science, Seoul National University Graduate School, Seoul, Korea. ; Cancer Research Institute, Seoul National University, Seoul, Korea.
  • Keun-Young Yoo
    Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea. ; Department of Biomedical Science, Seoul National University Graduate School, Seoul, Korea. ; Cancer Research Institute, Seoul National University, Seoul, Korea.
  • Sue K Park
    Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea. ; Department of Biomedical Science, Seoul National University Graduate School, Seoul, Korea. ; Cancer Research Institute, Seoul National University, Seoul, Korea.
  • Youdan Kim
    Department of Mechanical and Aerospace Engineering, Seoul National University College of Engineering, Seoul, Korea. ; Institute of Advanced Aerospace Technology, Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea.
  • Sungwan Kim
    Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.

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