Ensemble of Classifiers for Length of Stay Prediction in Colorectal Cancer

  • Ruxandra StoeanEmail author
  • Catalin Stoean
  • Adrian Sandita
  • Daniela Ciobanu
  • Cristian Mesina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)


The paper puts forward an ensemble of state-of-the-art classifiers – support vector machines, neural networks and decision trees – to estimate the length of stay after surgery in patients diagnosed with colorectal cancer. The three paradigms are brought together in order to achieve both a more accurate prediction through a voting scheme and transparency of the discriminative guidelines through visual rules. The results support the theoretical assumptions and are confirmed by the physicians.


Classification Length of stay Colorectal cancer Ensemble of methods Prediction accuracy Decision guidelines 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ruxandra Stoean
    • 1
    Email author
  • Catalin Stoean
    • 1
  • Adrian Sandita
    • 1
  • Daniela Ciobanu
    • 2
  • Cristian Mesina
    • 3
  1. 1.Department of Computer Science, Faculty of Mathematics and Natural SciencesUniversity of CraiovaCraiovaRomania
  2. 2.Department of Internal MedicineEmergency County Hospital Craiova and University of Medicine and Pharmacy of CraiovaCraiovaRomania
  3. 3.Department of SurgeryEmergency County Hospital Craiova and University of Medicine and Pharmacy of CraiovaCraiovaRomania

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