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SVM and SVM Ensembles in Breast Cancer Prediction

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  • Min-Wei Huang
  • Chih-Wen Chen
  • Wei-Chao Lin
  • Shih-Wen Ke
  • Chih-Fong Tsai

Abstract

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

Suggested Citation

  • Min-Wei Huang & Chih-Wen Chen & Wei-Chao Lin & Shih-Wen Ke & Chih-Fong Tsai, 2017. "SVM and SVM Ensembles in Breast Cancer Prediction," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
  • Handle: RePEc:plo:pone00:0161501
    DOI: 10.1371/journal.pone.0161501
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    Cited by:

    1. Maryam Mahsal Khan & Alexandre Mendes & Stephan K Chalup, 2018. "Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-15, February.
    2. Meshwa Rameshbhai Savalia & Jaiprakash Vinodkumar Verma, 2023. "Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 12(1), pages 1-19, January.
    3. Hosseinpour, Mahsa & Ghaemi, Sehraneh & Khanmohammadi, Sohrab & Daneshvar, Sabalan, 2022. "A hybrid high‐order type‐2 FCM improved random forest classification method for breast cancer risk assessment," Applied Mathematics and Computation, Elsevier, vol. 424(C).
    4. Liang Song & Shanjun Liu & Wenwen Li, 2019. "Quantitative Inversion of Fixed Carbon Content in Coal Gangue by Thermal Infrared Spectral Data," Energies, MDPI, vol. 12(9), pages 1-17, May.
    5. Cheong Kim & Francis Joseph Costello & Kun Chang Lee, 2019. "Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    6. Rongjun Chen & Jinhui Lin, 2020. "Identification of feature risk pathways of smoking-induced lung cancer based on SVM," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.

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