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Optimistic Multi-granulation Rough Set-Based Classification for Neonatal Jaundice Diagnosis

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Soft Computing Applications (SOFA 2014)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 356))

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Abstract

Neonatal jaundice diagnosis has been approached by various machine learning techniques. Pattern recognition algorithms are capable of improving the quality of prediction, early diagnosis of diseases, and disease classification. Pattern recognition algorithm results in Neonatal jaundice diagnosis or description of jaundice treatment by the medical specialist. This research focuses on applying rough set-based data mining techniques for Neonatal jaundice data to discover locally frequent identification of jaundice diseases. This work applies Optimistic Multi-granulation rough set model (OMGRS) for Neonatal jaundice data classification. Multi-granulation rough set provides efficient results than single granulation rough set model and soft rough set-based classifier model. The performance of the proposed Multi-granulation rough set-based classification is compared with other Naïve bayes, Back Propagation Neural Networks (BPN), and Kth Nearest Neighbor (KNN) approaches using various classification measures.

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Acknowledgments

The second author would like to thank UGC, New Delhi for the financial support received under UGC Major Research Project No. F-41-650/2012 (SR).

Author information

Authors and Affiliations

  1. Department of Computer Science, Periyar University, Salem, 636011, India

    S. Senthil Kumar & H. Hannah Inbarani

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. National Research Institute of Astronomy and Geophysics, Helwan, Egypt

    Hala S. Own

  4. Aurel Vlaicu University, Arad, Romania

    Valentina Emilia Balas

  5. Vasile Goldis West University of Arad, Satu Mare, Romania

    Teodora Olariu

Authors
  1. S. Senthil Kumar
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  2. H. Hannah Inbarani
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  3. Ahmad Taher Azar
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  4. Hala S. Own
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  5. Valentina Emilia Balas
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  6. Teodora Olariu
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Corresponding author

Correspondence to S. Senthil Kumar .

Editor information

Editors and Affiliations

  1. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. Faculty of Science and Technology, Data Science Institute, Bournemouth University, Poole, United Kingdom

    Lakhmi C. Jain

  3. University of Belgrade, Belgrade, Serbia

    Branko Kovačević

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Senthil Kumar, S., Hannah Inbarani, H., Azar, A.T., Own, H.S., Balas, V.E., Olariu, T. (2016). Optimistic Multi-granulation Rough Set-Based Classification for Neonatal Jaundice Diagnosis. In: Balas, V., C. Jain, L., Kovačević, B. (eds) Soft Computing Applications. SOFA 2014. Advances in Intelligent Systems and Computing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-319-18296-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-18296-4_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18295-7

  • Online ISBN: 978-3-319-18296-4

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