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  Published Paper Details:

  Paper Title

Cardiovascular Disease Prediction Using Machine Learning

  Authors

  Mrs.Prameela,  Acharya Samit,  Naveen Devadiga,  Prathwik

  Keywords

coronary heart disorder, cardiovascular sickness, AdaBoost algorithm, important element evaluation, linear discriminant analysis

  Abstract


Coronary sickness is a risky contamination that is spreading swiftly. Many humans suffer from it, and it's far presently the leading purpose of death worldwide. This ailment influences the coronary heart and other body components, so it calls for an green analysis to assist the scientific community in remedy. Early detection of this sickness can shop many lives via proper remedy. however, traditional diagnostic techniques including blood tests, electrocardiograms, cardiovascular computed tomography scans, magnetic resonance imaging of the coronary heart, and many others. are time-ingesting and invasive. on this review paper, diverse coronary ailment detection methods proposed in the past few years had been studied. The paper describes the techniques used by researchers and the accuracy claimed with the aid of them. A commonplace dataset became used to evaluate the claimed accuracy, and a assessment table of different strategies turned into provided inside the effects dialogue phase. The paper offers the strategies which have an ok stage of accuracy.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2401107

  Paper ID - 248761

  Page Number(s) - a833-a839

  Pubished in - Volume 12 | Issue 1 | January 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Mrs.Prameela,  Acharya Samit,  Naveen Devadiga,  Prathwik,   "Cardiovascular Disease Prediction Using Machine Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 1, pp.a833-a839, January 2024, Available at :http://www.ijcrt.org/papers/IJCRT2401107.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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