A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

Journal: Pacing and clinical electrophysiology : PACE
PMID: 38751036

Abstract

BACKGROUND: The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease.

Authors

  • Antony Jaya Mabel Rani
    Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
  • Chinnapillai Srivenkateswaran
    Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India.
  • Gurunathan Vishnupriya
    Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India.
  • Nalini Subramanian
    Department of Information Technology, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
  • Poonguzhali Ilango
    Department of ECE, Panimalar Engineering College, Chennai, Tamil Nadu, India.
  • Vijaya Kumar Jacintha
    Department of ECE, SRM Institute of Science & Technology, Chennai, Tamil Nadu, India.

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