Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 2157984, 12 pages
http://dx.doi.org/10.1155/2016/2157984
Research Article

Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression

1Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
2Research Center for Health Sciences, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
3Modeling of Non-Communicable Diseases Research Center, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
4Computer and IT Engineering Department, Shahrood University, Shahrood, Iran

Received 19 March 2016; Revised 1 August 2016; Accepted 5 September 2016

Academic Editor: Francesco Pappalardo

Copyright © 2016 Shahrbanoo Goli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. P. Klein and M. L. Moeschberger, Survival Analysis: Techniques for Censored and Truncated Data, Springer Science & Business Media, New York, NY, USA, 2003.
  2. A. Giordano, M. Giuliano, M. De Laurentiis et al., “Artificial neural network analysis of circulating tumor cells in metastatic breast cancer patients,” Breast Cancer Research and Treatment, vol. 129, no. 2, pp. 451–458, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. V. Van Belle, K. Pelckmans, S. van Huffel, and J. A. K. Suykens, “Improved performance on high-dimensional survival data by application of survival-SVM,” Bioinformatics, vol. 27, no. 1, pp. 87–94, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. C.-F. Huang, “A hybrid stock selection model using genetic algorithms and support vector regression,” Applied Soft Computing Journal, vol. 12, no. 2, pp. 807–818, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Kazem, E. Sharifi, F. K. Hussain, M. Saberi, and O. K. Hussain, “Support vector regression with chaos-based firefly algorithm for stock market price forecasting,” Applied Soft Computing, vol. 13, no. 2, pp. 947–958, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Tien Bui, B. Pradhan, O. Lofman, and I. Revhaug, “Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and nave bayes models,” Mathematical Problems in Engineering, vol. 2012, Article ID 974638, 26 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. P. K. Shivaswamy, W. Chu, and M. Jansche, “A support vector approach to censored targets,” in Proceedings of the 7th IEEE International Conference on Data Mining (ICDM '07), pp. 655–660, Omaha, Neb, USA, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. X. Ding, “The application of support vector machine in survival analysis,” in Proceedings of the 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC '11), pp. 6816–6819, IEEE, Dengfeng, China, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. V. Van Belle, K. Pelckmans, S. Van Huffel, and J. A. K. Suykens, “Support vector methods for survival analysis: a comparison between ranking and regression approaches,” Artificial Intelligence in Medicine, vol. 53, no. 2, pp. 107–118, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. F. M. Khan and V. Bayer-Zubek, “Support vector regression for censored data (SVRc): a novel tool for survival analysis,” in Proceedings of the 8th IEEE International Conference on Data Mining (ICDM '08), pp. 863–868, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. V. Van Belle, K. Pelckmans, J. A. Suykens, and S. Van Huffel, “Additive survival least-squares support vector machines,” Statistics in Medicine, vol. 29, no. 2, pp. 296–308, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. H. Mahjub, S. Goli, H. Mahjub, J. Faradmal, and A.-R. Soltanian, “Performance evaluation of support vector regression models for survival analysis: a simulation study,” International Journal of Advanced Computer Science & Applications, vol. 1, no. 7, pp. 381–389, 2016. View at Google Scholar
  13. I. Iguyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar · View at Scopus
  14. O. L. Mangasarian and G. Kou, “Feature selection for nonlinear kernel support vector machines,” in Proceedings of the 7th IEEE International Conference on Data Mining Workshops (ICDMW '07), IEEE, Omaha, Neb, USA, October 2007. View at Publisher · View at Google Scholar
  15. Y.-W. Chang and C.-J. Lin, “Feature ranking using linear SVM,” in WCCI Causation and Prediction Challenge, 2008. View at Google Scholar
  16. C.-T. Su and C.-H. Yang, “Feature selection for the SVM: an application to hypertension diagnosis,” Expert Systems with Applications, vol. 34, no. 1, pp. 754–763, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. M. H. Nguyen and F. De la Torre, “Optimal feature selection for support vector machines,” Pattern Recognition, vol. 43, no. 3, pp. 584–591, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. N. Becker, G. Toedt, P. Lichter, and A. Benner, “Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data,” BMC Bioinformatics, vol. 12, no. 1, article 138, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Hochreiter and K. Obermayer, “Nonlinear feature selection with the potential support vector machine,” in Feature Extraction, pp. 419–438, Springer, 2006. View at Google Scholar
  20. H.-X. Zhao and F. Magoulés, “Feature selection for support vector regression in the application of building energy prediction,” in Proceedings of the 9th IEEE International Symposium on Applied Machine Intelligence and Informatics (SAMI '11), pp. 219–223, Smolenice, Slovakia, January 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. G.-Z. Li, H.-H. Meng, M. Q. Yang, and J. Y. Yang, “Combining support vector regression with feature selection for multivariate calibration,” Neural Computing and Applications, vol. 18, no. 7, pp. 813–820, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. J.-B. Yang and C.-J. Ong, “Feature selection for support vector regression using probabilistic prediction,” in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '10), pp. 343–351, ACM, Washington, DC, USA, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Camps-Valls, A. M. Chalk, A. J. Serrano-López, J. D. Martín-Guerrero, and E. L. L. Sonnhammer, “Profiled support vector machines for antisense oligonucleotide efficacy prediction,” BMC Bioinformatics, vol. 5, no. 1, article 135, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Faradmal, A. Talebi, A. Rezaianzadeh, and H. Mahjub, “Survival analysis of breast cancer patients using cox and frailty models,” Journal of Research in Health Sciences, vol. 12, no. 2, pp. 127–130, 2012. View at Google Scholar · View at Scopus
  26. H. Kim and M. Bredel, “Feature selection and survival modeling in The Cancer Genome Atlas,” International Journal of Nanomedicine, vol. 8, supplement 1, pp. 57–62, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. J. Faradmal, A. R. Soltanian, G. Roshanaei, R. Khodabakhshi, and A. Kasaeian, “Comparison of the performance of log-logistic regression and artificial neural networks for predicting breast cancer relapse,” Asian Pacific Journal of Cancer Prevention, vol. 15, no. 14, pp. 5883–5888, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. J. Faradmal, A. Kazemnejad, R. Khodabakhshi, M.-R. Gohari, and E. Hajizadeh, “Comparison of three adjuvant chemotherapy regimes using an extended log-logistic model in women with operable breast cancer,” Asian Pacific Journal of Cancer Prevention, vol. 11, no. 2, pp. 353–358, 2010. View at Google Scholar · View at Scopus
  29. V. Lagani and I. Tsamardinos, “Structure-based variable selection for survival data,” Bioinformatics, vol. 26, no. 15, Article ID btq261, pp. 1887–1894, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. I. Choi, B. J. Wells, C. Yu, and M. W. Kattan, “An empirical approach to model selection through validation for censored survival data,” Journal of Biomedical Informatics, vol. 44, no. 4, pp. 595–606, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. Z. Liu, D. Chen, G. Tian, M.-L. Tang, M. Tan, and L. Sheng, “Efficient support vector machine method for survival prediction with SEER data,” in Advances in Computational Biology, vol. 680 of Advances in Experimental Medicine and Biology, pp. 11–18, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  32. X. Du and S. Dua, “Cancer prognosis using support vector regression in imaging modality,” World Journal of Clinical Oncology, vol. 2, no. 1, pp. 44–49, 2011. View at Google Scholar
  33. C. G. Moertel, T. R. Fleming, J. S. Macdonald et al., “Levamisole and fluorouracil for adjuvant therapy of resected colon carcinoma,” The New England Journal of Medicine, vol. 322, no. 6, pp. 352–358, 1990. View at Publisher · View at Google Scholar · View at Scopus
  34. T. M. Therneau and P. M. Grambsch, Modeling Survival Data: Extending the Cox Model, Springer Science & Business Media, 2000.
  35. V. Van Belle, K. Pelckmans, J. A. K. Suykens, and S. Van Huffel, “Learning transformation models for ranking and survival analysis,” The Journal of Machine Learning Research, vol. 12, pp. 819–862, 2011. View at Google Scholar · View at MathSciNet
  36. H.-T. Shiao and V. Cherkassky, “SVM-based approaches for predictive modeling of survival data,” in Proceedings of the International Conference on Data Mining (DMIN '13), The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), July 2013.
  37. V. Van Belle, K. Pelckmans, J. A. K. Suykens, and S. Van Huffel, “On the use of a clinical kernel in survival analysis,” in Proceedings of the 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN '10), pp. 451–456, Bruges, Belgium, April 2010. View at Scopus
  38. C. A. Bellera, G. MacGrogan, M. Debled, C. T. De Lara, V. Brouste, and S. Mathoulin-Pélissier, “Variables with time-varying effects and the Cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer,” BMC Medical Research Methodology, vol. 10, article 20, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. O. Hartmann, P. Schuetz, W. C. Albrich, S. D. Anker, B. Mueller, and T. Schmidt, “Time-dependent Cox regression: serial measurement of the cardiovascular biomarker proadrenomedullin improves survival prediction in patients with lower respiratory tract infection,” International Journal of Cardiology, vol. 161, no. 3, pp. 166–173, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. E. Bilal, J. Dutkowski, J. Guinney et al., “Improving breast cancer survival analysis through competition-based multidimensional modeling,” PLoS Computational Biology, vol. 9, no. 5, Article ID e1003047, 2013. View at Publisher · View at Google Scholar · View at Scopus
  41. A. Karimi, A. Delpisheh, K. Sayehmiri, H. Saboori, and E. Rahimi, “Predictive factors of survival time of breast cancer in kurdistan province of Iran between 2006–2014: a cox regression approach,” Asian Pacific Journal of Cancer Prevention, vol. 15, no. 19, pp. 8483–8488, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. J. Reunanen, “Overfitting in making comparisons between variable selection methods,” Journal of Machine Learning Research, vol. 3, pp. 1371–1382, 2003. View at Google Scholar · View at Scopus