title: Rough set theory based prognostic classification models for hospice referral.
abstract: BACKGROUND
  This paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respect to two factors related to clinical credibility: accuracy and accessibility. Accessibility refers to the ability of the model to provide traceable, interpretable results and use data that is relevant and simple to collect.
  
  
  METHODS
  We utilize retrospective data from 9,103 terminally ill patients to demonstrate the design and implementation RST- based models to identify potential hospice candidates. The classical rough set approach (CRSA) provides methods for knowledge acquisition, founded on the relational indiscernibility of objects in a decision table, to describe required conditions for membership in a concept class. On the other hand, the dominance-based rough set approach (DRSA) analyzes information based on the monotonic relationships between condition attributes values and their assignment to the decision class. CRSA decision rules for six-month patient survival classification were induced using the MODLEM algorithm. Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm.
  
  
  RESULTS
  The RST-based classifiers are compared with other predictive and rule based decision modeling techniques, namely logistic regression, support vector machines, random forests and C4.5. The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5.
  
  
  CONCLUSIONS
  This paper contributes to the growing body of research in RST-based prognostic models. RST and its extensions posses features that enhance the accessibility of clinical decision support models. While the non-rule-based methods-logistic regression, support vector machines and random forests-were found to achieve higher AUC, the performance differential may be outweighed by the benefits of the rule-based methods, particularly in the case of VC-DomLEM. Developing prognostic models for hospice referrals is a challenging problem resulting in substandard performance for all of the evaluated classification methods.
authors.affiliation: Threshold Tuning Optimization Team Citigroup, 3800 Citibank Center, Tampa, 33610, FL, USA.
authors.forename: Eleazar
authors.initials: E
authors.lastname: Gil-Herrera
authors.affiliation: Department of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, ENB 118, Tampa, 33620, FL, USA.
authors.forename: Garrick
authors.initials: G
authors.lastname: Aden-Buie
authors.affiliation: Department of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, ENB 118, Tampa, 33620, FL, USA. ayalcin@usf.edu.
authors.forename: Ali
authors.initials: A
authors.lastname: Yalcin
authors.affiliation: Threshold Tuning Optimization Team Citigroup, 3800 Citibank Center, Tampa, 33610, FL, USA.
authors.forename: Athanasios
authors.initials: A
authors.lastname: Tsalatsanis
authors.affiliation: Department of Systems and Information Engineering, University of Virginia, 151 Engineer's Way, Charlottesville, 22904, VA, USA.
authors.forename: Laura E
authors.initials: LE
authors.lastname: Barnes
authors.affiliation: Threshold Tuning Optimization Team Citigroup, 3800 Citibank Center, Tampa, 33610, FL, USA.|Department of Hematology and Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, University of South Florida, 12902 Magnolia Drive, Tampa, 33612, FL, USA.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Djulbegovic
mesh_terms: D000368:Aged; D002965:Classification; D005260:Female; D006738:Hospices / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011379:Prognosis; D012017:Referral and Consultation / Q000706:statistics & numerical data*; D019452:Terminally Ill / Q000706:statistics & numerical data*