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Classification Algorithms for Prediction of Lumbar Spine Pathologies

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Advanced Informatics for Computing Research (ICAICR 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 712))

Abstract

Classes can be predicted correctly in the dataset using Classification. For the present study, weka data mining tool is used to predict the lumbar spine pathologies. In this work dataset is firstly classified using different algorithms and then it is determined that which classification algorithm performs better for predicting lumbar spine pathologies. Lumbar spine diseases are predicted with identification of symptoms in patients. We have evaluated and compared six classification algorithms using different evaluation criteria. For the present work, the multilayer perceptron algorithm gives best results to predict the lumbar spine pathologies. This model can be used by the radiologists for lumbar spine pathologies prediction.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Lyallpur Khalsa College of Engineering, Jalandhar, Punjab, India

    Rajni Bedi

  2. Information Technology Department, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India

    Ajay Shiv Sharma

Authors
  1. Rajni Bedi
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  2. Ajay Shiv Sharma
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Corresponding author

Correspondence to Rajni Bedi .

Editor information

Editors and Affiliations

  1. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  2. IIT Roorkee, Roorkee, India

    Balasubramanian Raman

  3. CTIEMT, Jalandhar, Punjab, India

    Ashish Kumar Luhach

  4. Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, Nova Scotia, Canada

    Pawan Lingras

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© 2017 Springer Nature Singapore Pte Ltd.

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Bedi, R., Sharma, A.S. (2017). Classification Algorithms for Prediction of Lumbar Spine Pathologies. In: Singh, D., Raman, B., Luhach, A., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2017. Communications in Computer and Information Science, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-5780-9_4

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  • DOI: https://doi.org/10.1007/978-981-10-5780-9_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5779-3

  • Online ISBN: 978-981-10-5780-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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