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Simulation Study of Feature Selection on Survival Least Square Support Vector Machines with Application to Health Data

  • Dedy Dwi PrastyoEmail author
  • Halwa Annisa Khoiri
  • Santi Wulan Purnami
  • Suhartono
  • Soo-Fen Fam
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 937)

Abstract

One of semi parametric survival model commonly used is Cox Proportional Hazard Model (Cox PHM) that has some conditions must be satisfied, one of them is proportional hazard assumption among the category at each predictor. Unfortunately, the real case cannot always satisfy this assumption. One alternative model that can be employed is non-parametric approach using Survival Least Square-Support Vector Machine (SURLS-SVM). Meanwhile, the SURLS-SVM cannot inform which predictors are significant like the Cox PHM can do. To overcome this issue, the feature selection using backward elimination is employed by means of c-index increment. This paper compares two approaches, i.e. Cox PHM and SURLS-SVM, using c-index criterion applied on simulated and clinical data. The empirical results inform that the c-index of SURLS-SVM is higher than Cox PHM on both datasets. Furthermore, the simulation study is repeated 100 times. The simulation results show that the non-relevant predictors are often included in the model because the effect of confounding. For the application on clinical data (cervical cancer), the feature selection yields nine relevant predictors out of twelve predictors. The three predictors among the nine relevant predictors in SURLS-SVM are the significant predictors in Cox PHM.

Keywords

Survival Least square SVM Features selection Simulation Cervical cancer 

Notes

Acknowledgement

Authors thank to the reviewers for their advices. This research is supported by fundamental research scheme (PDUPT) in ITS number 871/PKS/ITS/2018 financed by DRPM DIKTI, Indonesian Ministry of Research, Technology and Higher Education (number 128/SP2H/PTNBH/DRPM/2018).

References

  1. 1.
    Kleinbaum, D.G., Klein, M.: Survival Analysis: A Self-Learning Text, 3rd edn. Springer, London (2012).  https://doi.org/10.1007/978-1-4419-6646-9CrossRefzbMATHGoogle Scholar
  2. 2.
    Mahjub, H., Faradmal, J., Goli, S., Soltanian, A.R.: Performance evaluation of support vector regression models for survival analysis: a simulation study. IJACSA 7(6), 381–389 (2016)Google Scholar
  3. 3.
    Van Belle, V., Pelckmans, K., Suykens, J.A., Van Huffel, S.: Support vector machines for survival analysis. In: Proceedings of the Third International Conference on Computational Intelligence in Medicine and Healthcare (CIMED), Plymouth (2007)Google Scholar
  4. 4.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM, Pittsburgh (1992)Google Scholar
  5. 5.
    Smola, A.J., Scholköpf, B.: A tutorial on support vector regression, statistics and computing. Stat. Comput. 14(3), 192–222 (2004)CrossRefGoogle Scholar
  6. 6.
    Van Belle, V., Pelckmans, K., Suykens, J.A., Van Huffel, S.: Additive survival least-squares support vector machines. Stat. Med. 29(2), 296–308 (2010)MathSciNetGoogle Scholar
  7. 7.
    Van Belle, V., Pelckmans, K., Suykens, J.A., Van Huffel, S.: Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artif. Intell. Med. 53(2), 107–118 (2011)CrossRefGoogle Scholar
  8. 8.
    Suykens, J.A., Vandewalle, J.: Least squares support vector machines classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRefGoogle Scholar
  9. 9.
    Goli, S., Mahjub, H., Faradmal, J.: Survival prediction and feature selection in patients with breast cancer using support vector regression. Comput. Math. Methods Med. 2016, 1–12 (2016)CrossRefGoogle Scholar
  10. 10.
    Khotimah, C., Purnami, S.W., Prastyo, D.D., Chosuvivatwong, V., Spriplung, H.: Additive survival least square support vector machines: a simulation study and its application to cervical cancer prediction. In: Proceedings of the 13th IMT-GT International Conference on Mathematics, Statistics and their Applications (ICMSA), AIP Conference Proceedings 1905 (050024), Kedah (2017)Google Scholar
  11. 11.
    Khotimah, C., Purnami, S.W., Prastyo, D.D.: Additive survival least square support vector machines and feature selection on health data in Indonesia. In: Proceedings of the International Conference on Information and Communications Technology (ICOIACT), IEEE Xplore (2018)Google Scholar
  12. 12.
    Haerdle, W.K., Prastyo, D.D., Hafner, C.M.: Support vector machines with evolutionary model selection for default prediction. In: Racine, J., Su, L., Ullah, A. (eds.) The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics, pp. 346–373. Oxford University Press, New York (2014)Google Scholar
  13. 13.
    Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 4(1), 16–28 (2014)CrossRefGoogle Scholar
  14. 14.
    Shieh, G.: Suppression situations in multiple linear regression. Educ. Psychol. Meas. 66(3), 435–447 (2006)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Bender, R., Augustin, T., Blettner, M.: Generating survival times to simulate Cox proportional hazards models. Stat. Med. 24(11), 1713–1723 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Haerdle, W.K., Prastyo, D.D.: Embedded predictor selection for default risk calculation: a Southeast Asian industry study. In: Chuen, D.L.K., Gregoriou, G.N. (eds.) Handbook of Asian Finance: Financial Market and Sovereign Wealth Fund, vol. 1, pp. 131–148. Academic Press, San Diego (2014)CrossRefGoogle Scholar
  17. 17.
    Suhartono, Saputri, P.D., Amalia, F.F., Prastyo, D.D., Ulama, B.S.S.: Model selection in feedforward neural networks for forecasting inflow and outflow in Indonesia. In: Mohamed, A., Berry, M., Yap, B. (eds.) Soft Computing and Data Science 2017. Communications in Computer and Information Science, vol. 788, pp. 95–105. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-7242-0_8CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dedy Dwi Prastyo
    • 1
    Email author
  • Halwa Annisa Khoiri
    • 1
  • Santi Wulan Purnami
    • 1
  • Suhartono
    • 1
  • Soo-Fen Fam
    • 2
  1. 1.Department of StatisticsInstitut Teknologi Sepuluh NopemberSurabayaIndonesia
  2. 2.Department of TechnopreneurshipUniversiti Teknikal Malaysia MelakaMelakaMalaysia

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