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Risk Prediction of Heart Disease Based on Swarm Optimized Neural Network

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Proceedings of International Conference on Computer Science and Information Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 255))

Abstract

Heart disease (HD) remains the biggest cause of deaths worldwide. This shows the importance of HD prediction at the early stage. In this paper, multi-layer feedforward neural network (MLFFNN) optimized with particle swarm optimization (PSO) is adopted for HD prediction at the early stage using the patient’s medical record. The network parameters considered for optimization are the number of hidden neurons, momentum factor, and learning rate. The efficiency of the PSO optimized neural network (PSONN) is calculated using the records collected from standard Cleveland database and Real time clinical dataset. The results show the proposed system can predict the likelihood of HD patients in a more efficient and accurate way.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, NooruI Islam Centre for Higher Education, Kanyakumari, India

    R. Chitra

  2. Department of Computer Science and Engineering, National Engineering College, Kovilpatti, India

    V. Seenivasagam

Authors
  1. R. Chitra
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  2. V. Seenivasagam
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Corresponding author

Correspondence to R. Chitra .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, SOA University, Bhubaneswar, India

    Srikanta Patnaik

  2. Electronics and Computer Engineering Technology, Indiana State University, Terre Haute, IN, USA

    Xiaolong Li

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Chitra, R., Seenivasagam, V. (2014). Risk Prediction of Heart Disease Based on Swarm Optimized Neural Network. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Computer Science and Information Technology. Advances in Intelligent Systems and Computing, vol 255. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1759-6_81

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  • DOI: https://doi.org/10.1007/978-81-322-1759-6_81

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1758-9

  • Online ISBN: 978-81-322-1759-6

  • eBook Packages: EngineeringEngineering (R0)

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