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Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique

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Computer Aided Intervention and Diagnostics in Clinical and Medical Images

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 31))

Abstract

The progression of the chronic kidney disease and methodologies to diagnose chronic kidney disease is a challenging problem which can reduce the cost of treatment. We studied 224 records of chronic kidney disease available on the UCI machine learning repository named chronic kidney diseases dating back to 2015. Our proposed method is based on deep neural network which predicts the presence or absence of chronic kidney disease with an accuracy of 97%. Compared to other available algorithms, the model we built shows better results which is implemented using the cross-validation technique to keep the model safe from overfitting. This automatic chronic kidney disease treatment helps reduce the kidney damage progression, but for this chronic kidney disease detection at initial stage is necessary.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, UV Patel College of Engineering, Ganpat University, Mehsana, 384012, Gujarat, India

    Himanshu Kriplani & Bhumi Patel

  2. Department of Computer Science and Engineering, Institute of Computer Technology, Ganpat University, Mehsana, 384012, Gujarat, India

    Sudipta Roy

  3. Department of Computer Science & Engineering, Calcutta University Technology Campus, Kolkata, 700098, India

    Sudipta Roy

Authors
  1. Himanshu Kriplani
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  2. Bhumi Patel
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  3. Sudipta Roy
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Corresponding author

Correspondence to Himanshu Kriplani .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    J. Dinesh Peter

  2. Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA

    Steven Lawrence Fernandes

  3. Brazilian Computer Society, Aalcides Platiny Alves Batista , Porto Alegre, Rio Grande do Sul, Brazil

    Carlos Eduardo Thomaz

  4. School of Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Serestina Viriri

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Kriplani, H., Patel, B., Roy, S. (2019). Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-04061-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04060-4

  • Online ISBN: 978-3-030-04061-1

  • eBook Packages: EngineeringEngineering (R0)

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