Experiment 1: Classifiers

  • Katarzyna A. TarnowskaEmail author
  • Zbigniew W. Ras
  • Pawel J. Jastreboff
Part of the Studies in Computational Intelligence book series (SCI, volume 685)


Following the dataset preprocessing, the next step in implementing RECTIN is classification module development. The classification module will use a model built on historical patients’ data, in order to support physicians in suggesting optimal treatment approach for new patients. Categorization is rather easy and relatively broad. However, a specific approach within each category varies. Before implementing this module, it is necessary to extract new, useful features and conduct experiments in order to obtain the most accurate classifier on the prepared dataset. It is assumed to reiterate the step of feature development in order to obtain the best combination of feature extraction/selection method and the prediction method. This involves the calibration and tuning of prediction methods, as well as comparing them and evaluating in terms of accuracy, F-score and confusion matrix.


Autism Spectrum Disorder Feature Selection Acoustic Neuroma Binary Attribute Medication Column 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Katarzyna A. Tarnowska
    • 1
    Email author
  • Zbigniew W. Ras
    • 1
  • Pawel J. Jastreboff
    • 2
  1. 1.Department of Computer ScienceUniversity of North Carolina at CharlotteCharlotteUSA
  2. 2.Department of OtolaryngologyEmory University School of MedicineAtlantaUSA

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