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Improving Children Diagnostics by Efficient Multi-label Classification Method

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Book cover Information Technologies in Medicine (ITiB 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 471))

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Abstract

Using intelligent computational methods may support children diagnostics process. As in many cases patients are affected by multiple illnesses, multi-perspective view on patient data is necessary to improve medical decision making. In the paper, multi-label classification method—Labels Chain is considered. It performs well when the number of attributes significantly exceeds the number of instances. The effectiveness of the method is checked by experiments conducted on real data. The obtained results are evaluated by using two metrics: Classification Accuracy and Hamming Loss, and compared to the effects of the most popular techniques: Binary Relevance and Label Power-set.

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Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Wólczańska 215, Lodz, Poland

    Kinga Glinka, Agnieszka Wosiak & Danuta Zakrzewska

Authors
  1. Kinga Glinka
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  2. Agnieszka Wosiak
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  3. Danuta Zakrzewska
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Corresponding author

Correspondence to Agnieszka Wosiak .

Editor information

Editors and Affiliations

  1. Fac of Biomed Engg, ul. Roosevelta 40, Silesian University of Technology, Gliwice, Poland

    Ewa Piętka

  2. Fac of Biomed Engg, ul. Roosevelta 40, Silesian University of Technology, Gliwice, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

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Glinka, K., Wosiak, A., Zakrzewska, D. (2016). Improving Children Diagnostics by Efficient Multi-label Classification Method. In: Piętka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technologies in Medicine. ITiB 2016. Advances in Intelligent Systems and Computing, vol 471. Springer, Cham. https://doi.org/10.1007/978-3-319-39796-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-39796-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39795-5

  • Online ISBN: 978-3-319-39796-2

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