Interval Coded Scoring Index with Interaction Effects - A Sensitivity Study

Lieven Billiet, Sabine Van Huffel, Vanya Van Belle

Abstract

Scoring systems have been used since long in medical practice, but often they are based on experience rather than a structural approach. In literature, the interval coded scoring index (ICS) has been introduced as an alternative. It derives a scoring system from data using optimization techniques. This work discusses an extension, ICS*, that takes variable interactions into account. Furthermore, a study is performed to give insight into the new model’s sensitivity to noise, the size of the data set and the number of non-informative variables. The study shows interactions can mostly be discovered robustly, even in the presence of noise and spurious variables. A final validation on two UCI data sets further indicates the quality of the approach.

References

  1. Chowriappa, P., Dua, S., and Todorov, Y. (2014). Introduction to machine learning in healthcare informatics. In Machine Learning in Healthcare Informatics, pages 1-23. Springer.
  2. da Rocha Neto, A. R., Sousa, R., de A. Barreto, G., and Cardoso, J. S. (2011). Diagnostic of pathology on the vertebral column with embedded reject option. In Vitri, J., Sanches, J., and Hernndez, M., editors, Pattern Recognition and Image Analysis, volume 6669 of Lecture Notes in Computer Science, pages 588-595. Springer Berlin Heidelberg.
  3. Davenport, M., Duarte, M., Eldar, Y., Kutyniok, G., et al. (2012). Compressed sensing: theory and applications. Cambridge University Press Cambridge.
  4. Duch, W., Adamczak, R., Grabczewski, K., Ishikawa, M., and Ueda, H. (1997). Extraction of crisp logical rules using constrained backpropagation networks. In Proc. of the European Symposium on Artificial Neural Networks (ESANN).
  5. Jeong, B.-H., Koh, W.-J., Yoo, H., Um, S.-W., Suh, G. Y., Chung, M. P., Kim, H., Kwon, O. J., and Jeon, K. (2013). Performances of prognostic scoring systems in patients with healthcare-associated pneumonia. Clinical Infectious Diseases, 56(5):625-632.
  6. Lichman, M. (2013). UCI machine learning repository. http://archive.ics.uci.edu/ml. last accessed 20/5/2015.
  7. Mounzer, R., Langmead, C. J., Wu, B. U., Evans, A. C., Bishehsari, F., Muddana, V., Singh, V. K., Slivka, A., Whitcomb, D. C., Yadav, D., Banks, P. A., and Papachristou, G. I. (2012). Comparison of existing clinical scoring systems to predict persistent organ failure in patients with acute pancreatitis. Gastroenterology, 142(7):1476 - 1482.
  8. Simon, N., Friedman, J., Hastie, T., and Tibshirani, R. (2013). A sparse-group lasso. Journal of Computational and Graphical Statistics.
  9. Sra, S. (2006). Efficient large scale linear programming support vector machines. In ECML 2006, pages 767-774, Berlin, Germany. Max-Planck-Gesellschaft, Springer.
  10. Suykens, J. A., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J., Suykens, J., and Van Gestel, T. (2002). Least squares support vector machines, volume 4. World Scientific.
  11. Ustun, B., Trac, S., and Rudin, C. (2013). Supersparse linear integer models for predictive scoring systems. In Proceeding of the 27th AAAI Conference on Artificial Intelligence (AAAI-13), pages 128-130.
  12. Van Belle, V., Van Calster, B., Timmerman, D., Bourne, T., Bottomley, C., Valentin, L., Neven, P., Van Huffel, S., Suykens, J. A. K., and Boyd, S. (2012). A mathematical model for interpretable clinical decision support with applications in gynecology. PLoS ONE, 7(3):e34312.
  13. Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc.
  14. Yang, H.-I., Yuen, M.-F., Chan, H. L.-Y., Han, K.-H., Chen, P.-J., Kim, D.-Y., Ahn, S.-H., Chen, C.-J., Wong, V. W.-S., and Seto, W.-K. (2011). Risk estimation for hepatocellular carcinoma in chronic hepatitis b (reachb): development and validation of a predictive score. The Lancet Oncology, 12(6):568 - 574.
Download


Paper Citation


in Harvard Style

Billiet L., Huffel S. and Belle V. (2016). Interval Coded Scoring Index with Interaction Effects - A Sensitivity Study . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 33-40. DOI: 10.5220/0005646500330040


in Bibtex Style

@conference{icpram16,
author={Lieven Billiet and Sabine Van Huffel and Vanya Van Belle},
title={Interval Coded Scoring Index with Interaction Effects - A Sensitivity Study},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={33-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005646500330040},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Interval Coded Scoring Index with Interaction Effects - A Sensitivity Study
SN - 978-989-758-173-1
AU - Billiet L.
AU - Huffel S.
AU - Belle V.
PY - 2016
SP - 33
EP - 40
DO - 10.5220/0005646500330040