Predicting Outcome of Ischemic Stroke Patients using Bootstrap Aggregating with M5 Model Trees

Ahmedul Kabir, Carolina Ruiz, Sergio A. Alvarez, Majaz Moonis

2017

Abstract

The objective of our study is to predict the clinical outcome of ischemic stroke patients after 90 days of stroke using the modified Rankin Scale (mRS) score. After experimentation with various regression techniques, we discovered that using M5 model trees to predict the score and then using bootstrap aggregating as a meta-learning technique produces the best prediction results. The same regression when followed by classification also performs better than regular multi-class classification. In this paper, we present the methodology used, and compare the results with other standard predictive techniques. We also analyze the results to provide insights on the factors that affect stroke outcomes.

References

  1. Adams, H. P., Bendixen, B. H., Kappelle, L. J., Biller, J., Love, B. B., Gordon, D. L. & Marsh, E. E. 1993. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke, 24, 35-41.
  2. Aslam, J. A., Popa, R. A. & Rivest, R. L. 2007. On Estimating the Size and Confidence of a Statistical Audit. USENIX/ACCURATE Electronic Voting Technology Workshop, 7, 8.
  3. Banks, J. L. & Marotta, C. A. 2007. Outcomes validity and reliability of the modified Rankin scale: Implications for stroke clinical trials a literature review and synthesis. Stroke, 38, 1091-1096.
  4. Breiman, L. 1996. Bagging predictors. Machine learning, 24, 123-140.
  5. Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. 1984. Classification and regression trees, CRC press.
  6. Brott, T., Adams, H., Olinger, C. P., Marler, J. R., Barsan, W. G., Biller, J., Spilker, J., Holleran, R., Eberle, R. & Hertzberg, V. 1989. Measurements of acute cerebral infarction: a clinical examination scale. Stroke, 20, 864-870.
  7. Brown, A. W., Therneau, T. M., Schultz, B. A., Niewczyk, P. M. & Granger, C. V. 2015. Measure of functional independence dominates discharge outcome prediction after inpatient rehabilitation for stroke. Stroke, 46, 1038-1044.
  8. Etemad-Shahidi, A. & Mahjoobi, J. 2009. Comparison between M5' model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Engineering, 36, 1175-1181.
  9. Gialanella, B., Santoro, R. & Ferlucci, C. 2013. Predicting outcome after stroke: the role of basic activities of daily living predicting outcome after stroke. European journal of physical and rehabilitation medicine, 49, 629-637.
  10. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. & Witten, I. H. 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11, 10-18.
  11. Henninger, N., Lin, E., Baker, S. P., Wakhloo, A. K., Takhtani, D. & Moonis, M. 2012. Leukoaraiosis predicts poor 90-day outcome after acute large cerebral artery occlusion. Cerebrovascular Diseases, 33, 525-531.
  12. Keith, R., Granger, C., Hamilton, B. & Sherwin, F. 1987. The functional independence measure. Adv Clin Rehabil, 1, 6-18.
  13. Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI.
  14. Marini, C., De Santis, F., Sacco, S., Russo, T., Olivieri, L., Totaro, R. & Carolei, A. 2005. Contribution of atrial fibrillation to incidence and outcome of ischemic stroke results from a population-based study. Stroke, 36, 1115-1119.
  15. McCullagh, P. 1980. Regression models for ordinal data. Journal of the royal statistical society. Series B (Methodological), 109-142.
  16. Moonis, M., Kane, K., Schwiderski, U., Sandage, B. W. & Fisher, M. 2005. HMG-CoA reductase inhibitors improve acute ischemic stroke outcome. Stroke, 36, 1298-1300.
  17. Moore, D. S. 2007. The basic practice of statistics, New York, WH Freeman
  18. Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., Das, S. R., de Ferranti, S., Després, J.-P. & Fullerton, H. J. 2016. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation, 133, 447.
  19. Nakayama, H., Jørgensen, H., Raaschou, H. & Olsen, T. 1994. The influence of age on stroke outcome. The Copenhagen Stroke Study. Stroke, 25, 808-813.
  20. Nogueira, R. G., Liebeskind, D. S., Sung, G., Duckwiler, G., Smith, W. S. & Multi MERCI Writing Committee 2009. Predictors of good clinical outcomes, mortality, and successful revascularization in patients with acute ischemic stroke undergoing thrombectomy pooled analysis of the Mechanical Embolus Removal in Cerebral Ischemia (MERCI) and Multi MERCI Trials. Stroke, 40, 3777-3783.
  21. Quinlan, J. R. 1992. Learning with continuous classes. 5th Australian joint conference on artificial intelligence. Singapore.
  22. Quinlan, J. R. 1993. C4. 5 Programs for Machine Learning, San Francisco, Morgan Kauffmann.
  23. Raffeld, M. R., Debette, S. & Woo, D. 2016. International Stroke Genetics Consortium Update. Stroke, 47, 1144- 1145.
  24. Rankin, J. 1957. Cerebral vascular accidents in patients over the age of 60. II. Prognosis. Scottish medical journal, 2, 200.
  25. Rodgers, J. L. & Nicewander, W. A. 1988. Thirteen ways to look at the correlation coefficient. The American Statistician, 42, 59-66.
  26. Tan, P.-N., Steinbach, M. & Kumar, V. 2005. Introduction to data mining, Boston, Addison-Wesley.
  27. Van Swieten, J., Koudstaal, P., Visser, M., Schouten, H. & Van Gijn, J. 1988. Interobserver agreement for the assessment of handicap in stroke patients. Stroke, 19, 604-607.
  28. Wang, Y. & Witten, I. H. 1996. Induction of model trees for predicting continuous classes. European Conference on Machine Learning. University of Economics, Prague.
  29. Willmott, C. J. & Matsuura, K. 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30, 79-82.
  30. Yong, M. & Kaste, M. 2008. Dynamic of hyperglycemia as a predictor of stroke outcome in the ECASS-II trial. Stroke, 39, 2749-2755.
Download


Paper Citation


in Harvard Style

Kabir A., Ruiz C., Alvarez S. and Moonis M. (2017). Predicting Outcome of Ischemic Stroke Patients using Bootstrap Aggregating with M5 Model Trees . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 178-187. DOI: 10.5220/0006282001780187


in Bibtex Style

@conference{healthinf17,
author={Ahmedul Kabir and Carolina Ruiz and Sergio A. Alvarez and Majaz Moonis},
title={Predicting Outcome of Ischemic Stroke Patients using Bootstrap Aggregating with M5 Model Trees},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={178-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006282001780187},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - Predicting Outcome of Ischemic Stroke Patients using Bootstrap Aggregating with M5 Model Trees
SN - 978-989-758-213-4
AU - Kabir A.
AU - Ruiz C.
AU - Alvarez S.
AU - Moonis M.
PY - 2017
SP - 178
EP - 187
DO - 10.5220/0006282001780187