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Classifying highly imbalanced ICU data

Author

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  • Yazan Roumani
  • Jerrold May
  • David Strum
  • Luis Vargas

Abstract

Highly imbalanced data sets are those where the class of interest is rare. In this paper, we compare the performance of several common data mining methods, logistic regression, discriminant analysis, Classification and Regression Tree (CART) models, C5, and Support Vector Machines (SVM) in predicting the discharge status (alive or deceased, with “deceased” being the class of interest) of patients from an Intensive Care Unit (ICU). Using a variety of misclassification cost ratio (MCR) values and using specificity, recall, precision, the F-measure, and confusion entropy (CEN) as criteria for evaluating each method’s performance, C5 and SVM performed better than the other methods. At a MCR of 100, C5 had the highest recall and SVM the highest specificity and lowest CEN. We also used Hand’s measure to compare the five methods. According to Hand’s measure, logistic regression performed the best. This article makes several contributions. We show how the use of MCR for analyzing imbalanced medical data significantly improves the method’s classification performance. We also found that the F-measure and precision did not improve as the MCR was increased. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Yazan Roumani & Jerrold May & David Strum & Luis Vargas, 2013. "Classifying highly imbalanced ICU data," Health Care Management Science, Springer, vol. 16(2), pages 119-128, June.
    • Handle: RePEc:kap:hcarem:v:16:y:2013:i:2:p:119-128
    DOI: 10.1007/s10729-012-9216-9
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    Cited by:

    1. Yazan F. Roumani & Yaman Roumani & Joseph K. Nwankpa & Mohan Tanniru, 2018. "Classifying readmissions to a cardiac intensive care unit," Annals of Operations Research, Springer, vol. 263(1), pages 429-451, April.
    2. Jie Bai & Andreas Fügener & Jan Schoenfelder & Jens O. Brunner, 2018. "Operations research in intensive care unit management: a literature review," Health Care Management Science, Springer, vol. 21(1), pages 1-24, March.
    3. Daniel Gartner & Rainer Kolisch & Daniel B. Neill & Rema Padman, 2015. "Machine Learning Approaches for Early DRG Classification and Resource Allocation," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 718-734, November.
    4. Talayeh Razzaghi & Ilya Safro & Joseph Ewing & Ehsan Sadrfaridpour & John D. Scott, 2019. "Predictive models for bariatric surgery risks with imbalanced medical datasets," Annals of Operations Research, Springer, vol. 280(1), pages 1-18, September.

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