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Classifying Leukemia and Gout Patients with Neural Networks

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Book cover Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 903))

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Abstract

Machine Learning is one of the top growing fields of recent times and is applied in various areas such as healthcare. In this article, machine learning is used to study the patients suffering from either gout or leukemia, but not both, with the use of their uric acid signatures. The study of the uric acid signatures involves the application of supervised machine learning, using an artificial neural network (ANN) with one hidden layer and sigmoid activation function, to classify patients and the calculation of the accuracy with k-fold cross validation. We identify the number of nodes in the hidden layer and a value for the weight decay parameter that are optimal in terms of accuracy and ensure good performance.

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Notes

  1. 1.

    In R, duplicated function with logical negation operator (!) is used to find unique SUBJECT_IDs.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Göttingen, Göttingen, Germany

    Guryash Bahra & Lena Wiese

Authors
  1. Guryash Bahra
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  2. Lena Wiese
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Corresponding author

Correspondence to Lena Wiese .

Editor information

Editors and Affiliations

  1. University of Tunis, Tunis, Tunisia

    Mourad Elloumi

  2. MiCS, Media Computer Science, University of Passau, Passau, Bayern, Germany

    Michael Granitzer

  3. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Twente, Enschede, Overijssel, The Netherlands

    Christin Seifert

  5. Fak. Medien, Bauhaus Universität Weimar, Weimar, Thüringen, Germany

    Benno Stein

  6. Inst. für Softwaretechnik, Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  7. FAW, Johannes Kepler University of Linz, Linz, Austria

    Roland Wagner

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Bahra, G., Wiese, L. (2018). Classifying Leukemia and Gout Patients with Neural Networks. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-99133-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99132-0

  • Online ISBN: 978-3-319-99133-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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