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Machine Learning Methods for Dialysis Therapy Decision Problem — Comparative Study

  • Conference paper
Computer Recognition Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 30))

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Abstract

The main goal of our research was attempt to answer the question, if machine learning algorithms could be applied in computer aided medical diagnostics. Executed tests proven, that data mining methods based on machine learning can be used in medical diagnostics, but it can not substitute an expert, especially in case of rare diseases.

Classifiers induced from revised datasets have better classification accuracy. It is indicating that quality of training data has significant influence for induced classifier accuracy.

In expert opinion of expert, the most of obtained decision rules are consistent with medical knowledge. All cases of incorrect classification were caused by insufficient mathematical model. The evaluation of exact mathematical model in medicine is very difficult.

In this paper results of experiments on two popular machine learning algorithms were presented. The appliance of other classification methods, like Bayesian classifiers, neural networks and fuzzy sets, is subject of future research.

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References

  1. Januszewicz Wlodzimierz, Kokot F. (red.), Internal Medicine, Medical Publishers PZWL, Warsaw 2002 (in polish)

    Google Scholar 

  2. Mitchell Tom, Machine Learning, McGraw Hill, 1997

    Google Scholar 

  3. Orlowski Tadeusz (red.), Kidney Diseases, Medical Publishers PZWL, Warsaw 1997 (in polish)

    Google Scholar 

  4. Quinlan J. Ross, C4.5: Programs For Machine Learning, Morgan Kaufmann Publishers, San Mateo, California, 1993

    Google Scholar 

  5. Rutkowski Boleslaw, Czekalski S., Guidelines for Kidney Diseases Diagnosis and Treatment, Medical Publishers Makmed, Gdansk 2001 (in polish)

    Google Scholar 

  6. Witten Ian H., Eibe F., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, 2000

    Google Scholar 

  7. Clark Peter, Niblett Tim, The CN2 Induction Algorithm, Machine Learning Journal, 3(4), pp261–283, 1989

    Google Scholar 

  8. Clark Peter, Boswell R., Rule Induction Witch CN2: Some Recent Improvements, Machine Learning-Proceedings of the Fifth European Conference (EWSL-31), pp151–163, 1991

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Penar, W., Wozniak, M. (2005). Machine Learning Methods for Dialysis Therapy Decision Problem — Comparative Study. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_77

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  • DOI: https://doi.org/10.1007/3-540-32390-2_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25054-8

  • Online ISBN: 978-3-540-32390-7

  • eBook Packages: EngineeringEngineering (R0)

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