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Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features

Author

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  • Gregor Stiglic
  • Petra Povalej Brzan
  • Nino Fijacko
  • Fei Wang
  • Boris Delibasic
  • Alexandros Kalousis
  • Zoran Obradovic

Abstract

Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755–0.771) to 0.769 (95% CI: 0.761–0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.

Suggested Citation

  • Gregor Stiglic & Petra Povalej Brzan & Nino Fijacko & Fei Wang & Boris Delibasic & Alexandros Kalousis & Zoran Obradovic, 2015. "Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0144439
    DOI: 10.1371/journal.pone.0144439
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    References listed on IDEAS

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    1. Radchenko, Peter & James, Gareth M., 2010. "Variable Selection Using Adaptive Nonlinear Interaction Structures in High Dimensions," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1541-1553.
    2. Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, vol. 28(3), pages 722-738.
    3. Choi, Nam Hee & Li, William & Zhu, Ji, 2010. "Variable Selection With the Strong Heredity Constraint and Its Oracle Property," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 354-364.
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    Cited by:

    1. Feihan Lu & Yao Zheng & Harrington Cleveland & Chris Burton & David Madigan, 2018. "Bayesian hierarchical vector autoregressive models for patient-level predictive modeling," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-27, December.

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