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Journals / Mugla Journal of Science and Technology / 2020 / Cilt: 6 - Sayı: 2

USE OF ENSEMBLE METHODS FOR SURVIVAL PREDICTION

Aslıhan ŞENTÜRK ACAR
Nihal ATA TUTKUN
Pages
158–164
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Based on case study results, machine learning methods are promising alternatives for survival prediction.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Cox regression model is used for modelling censored data to investigate the association between the survival time and covariates.
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Key Findings & Data:In this study, Turkish breast cancer data set is used to compare the predictive performance of Cox regression model and ensemble machine learning methods.
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Conclusion & Impact:Based on case study results, machine learning methods are promising alternatives for survival prediction.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Censored data · Cox regression model · Machine learning

Abstract

Cox regression model is used for modelling censored data to investigate the association between the survival time and covariates. It is important to assess the fit of Cox regression model since it has a key assumption called proportional hazards. Violation of this assumption induces an invalid model and changes the interpretation of the results. When the objective is the risk prediction, various machine learning methods can be good alternatives to Cox regression model due to their flexible structure. In this study, Turkish breast cancer data set is used to compare the predictive performance of Cox regression model and ensemble machine learning methods. Integrated Brier score is used to measure the predictive performance of candidate models. Based on case study results, machine learning methods are promising alternatives for survival prediction.

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Cite This Article

ACAR, A. Ş., TUTKUN, N. A. (2020). USE OF ENSEMBLE METHODS FOR SURVIVAL PREDICTION. Mugla Journal of Science and Technology, Cilt: 6 - Sayı: 2, 158–164. https://ajindex.com/articles/340994

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References (17)

  1. Clark, T. G., Bradburn, M. J., Love S. B., and. Altman, D. G, “Survival analysis part I: basic concepts and first analyses,” Br. J. Cancer, vol. 89, no. 2, pp. 232–38, Jul. 2003. Babińska, M., Chudek, J., Elżbieta Chełmecka3, Janik, M., Klimek, K., and Owczarek, A., “Limitations of Cox Proportional Hazards Analysis in Mortality Prediction of Patients with Acute Coronary Syndrome", Studies in Logic, Grammar and Rhetoric, 2018.
  2. Nardi, A. and Schemper M., “Comparing Cox and parametric models in clinical studies,” Stat. Med., vol. 22, no. 23, pp. 3597–3610, Dec. 2003.
  3. Kleinbaum, D.G., Survival Analysis - A Self-Learning Text, Springer, 2010.
  4. Radespiel-Tröger, M., Rabenstein T., Schneider, H. T. and Lausena B., “Comparison of tree-based methods for prognostic stratification of survival data,”Artificial Intelligence in Medicine, 28(3), pp.323-341, 2003.
  5. Zhou, Y. and McArdle, J. J., “Rationale and Applications of Survival Tree and Survival Ensemble Methods,” Psychometrika, vol. 80, no. 3, pp. 811–833, Sep. 2015.
  6. Hu, C., and Steingrimsson, J. A., “Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests,” J. Biopharm. Stat., vol. 28, no. 2, pp. 333–349, Mar. 2018.
  7. Paraschiakos, F., “Machine learning for survival analysis on clinical data,” Master's thesis, 2016.
  8. Wang, P., Li, Y. and Reddy C. K., “Machine Learning for Survival Analysis: A Survey", ACM Computing Surveys (CSUR), 51(6):1-36, 2019.
  9. Breiman, L., “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, 1996.
  10. Breiman, L., “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
  11. Schapire, R.E., “A brief introduction to boosting,” in Ijcai, vol. 99, pp. 1401–1406, 1999.
  12. Kuhn, M. and Johnson, K., Applied predictive modeling, vol. 26. Springer, 2013.
  13. Hothorn, T., Lausen, B., Benner, A. and Radespiel-Tröger, M. “Bagging survival trees,” Stat. Med., vol. 23, no. 1, pp. 77–91, Jan. 2004.
  14. Ishwaran, H., Kogalur, U. B., Blackstone, E. H. and Lauer, M. S. “Random survival forests,” Ann. Appl. Stat., vol. 2, no. 3, pp. 841–860, Sep. 2008.
  15. Hothorn, T., Bühlmann P., Dudoit, S., Molinaro A. and van der Laan M. J., “Survival ensembles,” Biostat. Oxf. Engl., vol. 7, no. 3, pp. 355–373, Jul. 2006.
  16. Graf, E., Schmoor, C., Sauerbrei, W. and Schumacher M., “Assessment and comparison of prognostic classification schemes for survival data,” Stat. Med., vol. 18, no. 17–18, pp. 2529–2545, 1999.
  17. Mogensen, U. B., Ishwaran, H. and Gerds, T.