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Using a Hybrid Model of Machine LearningAlgorithms for Efficient Cardiovascular illness Prediction

    Authors

    • Hari Krishna T 1
    • Maimoon S 2
    • Naveena Jyothi J 2
    • RaviSankar Reddy R 2
    • Pavani C 2
    • Narendra Kumar Raju K 2

    1 Associate Professor & Head Department of Artificial Intelligence and Machine Learning, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India

    2 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India

,

Document Type : Research Article

10.47392/irjash.2023.S064
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Abstract

Researchers have paid more attention to the field of medicine. Researchers have found several kinds of factors which leads to human early mortality. According to the relevant studies, illnesses are brought on by a variety of fac- tors and heart-related illnesses is one of them. Numerous scholars suggested unconventional ways to prolong human life and aid medical professionals in the diagnosis, treatment and management of cardiac disease. Some practical techniques help the expert make a choice, but every effective plan contains some drawbacks. The suggested techniques in this paper examines an act of Decision Tree, Random Forest, XGBoost and Hybrid Model. Based on the results, we created a hybrid approach to archive data with more precision.

Keywords

  • Machine learning
  • Classification Technique
  • Decision Tree
  • Random Forest
  • XGBoost
  • supervised machine learn- ing
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International Research Journal on Advanced Science Hub
Volume 5, Issue 05S - Issue Serial Number 5
May 2023
Page 483-488
Files
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  • PDF 2.38 M
History
  • Receive Date: 26 February 2023
  • Revise Date: 12 March 2023
  • Accept Date: 21 March 2023
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  • Article View: 184
  • PDF Download: 78

APA

T, H. K. , S, M. , J, N. J. , R, R. R. , C, P. and K, N. K. R. (2023). Using a Hybrid Model of Machine LearningAlgorithms for Efficient Cardiovascular illness Prediction. International Research Journal on Advanced Science Hub, 5(Issue 05S), 483-488. doi: 10.47392/irjash.2023.S064

MLA

T, H. K. , , S, M. , , J, N. J. , , R, R. R. , , C, P. , and K, N. K. R. . "Using a Hybrid Model of Machine LearningAlgorithms for Efficient Cardiovascular illness Prediction", International Research Journal on Advanced Science Hub, 5, Issue 05S, 2023, 483-488. doi: 10.47392/irjash.2023.S064

HARVARD

T, H. K., S, M., J, N. J., R, R. R., C, P., K, N. K. R. (2023). 'Using a Hybrid Model of Machine LearningAlgorithms for Efficient Cardiovascular illness Prediction', International Research Journal on Advanced Science Hub, 5(Issue 05S), pp. 483-488. doi: 10.47392/irjash.2023.S064

CHICAGO

H. K. T , M. S , N. J. J , R. R. R , P. C and N. K. R. K, "Using a Hybrid Model of Machine LearningAlgorithms for Efficient Cardiovascular illness Prediction," International Research Journal on Advanced Science Hub, 5 Issue 05S (2023): 483-488, doi: 10.47392/irjash.2023.S064

VANCOUVER

T, H. K., S, M., J, N. J., R, R. R., C, P., K, N. K. R. Using a Hybrid Model of Machine LearningAlgorithms for Efficient Cardiovascular illness Prediction. International Research Journal on Advanced Science Hub, 2023; 5(Issue 05S): 483-488. doi: 10.47392/irjash.2023.S064

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