Art-Based Autonomous Learning Systems: Part II — Applications

  • C. P. Lim
  • R. F. Harrison
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 43)


This chapter presents an evaluation of the practical applicability of the multiple classifier systems developed in Part I. First, a benchmark study is conducted to compare the system performance with other machine learning algorithms. Then, two medical decision-making problems comprising real patient records collected from a number of hospitals are investigated, viz. (i) the prognosis of patients exhibiting complications admitted to coronary care units, and (ii) the prediction of survival rates among trauma patients. Various operating strategies including off-line and on-line learning are examined, and implications of the results are discussed. In particular, the outcomes reveal the potential of employing such an autonomously learning system as a useful decision support tool in the medical arena.


Multiple Classifier Coronary Care Unit Confusion Matrice Learn Classifier System Revise Trauma Score 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • C. P. Lim
    • 1
  • R. F. Harrison
    • 2
  1. 1.School of Industrial TechnologyUniversiti Sains MalaysiaPenangMalaysia
  2. 2.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUK

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