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Front. Genet. | doi: 10.3389/fgene.2019.00848

Developing a Novel Machine Learning-based Classification Scheme for Predicting SPCs in Breast Cancer Survivors

  • 1Chung Shan Medical University, Taiwan
  • 2Department of Industrial Engineering and Management, Ming Chi University of Technology, Taiwan

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with breast cancer. This study was conducted to develop a novel machine learning-based classification scheme for predicting the risk factors of SPCs in breast cancer survivors. The proposed scheme was based on the XGBoost classifier with the following four comparable strategies: transformation, resampling, clustering, and ensemble learning to improve the training balanced accuracy. Results suggested that the best prediction accuracy for an empirical case is the XGBoost associates with the strategies of resampling and clustering. The experimental results showed that age, sequence of radiotherapy and surgery, surgical margins of the primary site, human epidermal growth factor, high-dose clinical target volume, and estrogen receptor are relatively more important risk factors associated with SPCs in patients with breast cancer. These risk factors should be monitored for the early detection of breast cancer. In conclusion, the proposed scheme can support the important influence of personality and clinical symptom representations on all phases of primary treatment trajectory. Our results further suggest that adaptive machine learning techniques require the incorporation of significant variables for optimal prediction.

Keywords: Second Primary Cancers (SPCs), breast cancer, machine learning, Classification, machine learning-based classification scheme

Received: 05 Jul 2019; Accepted: 14 Aug 2019.

Copyright: © 2019 Chang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Chi-Chang Chang, Chung Shan Medical University, Taichung, 40201, Taiwan,
Mx. Ssu-Han Chen, Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei, Taiwan,