UGC Approved Journal no 63975(19)

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Published in:

Volume 10 Issue 10
October-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2310544


Registration ID:
526841

Page Number

g378-g383

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Title

An Efficient Machine Learning Technique for Prediction of Cardiac Diseases using ECG Dataset

Abstract

The heart is significant organ of human body part heart disease describes a scope of conditions that influence your heart. Machine learning (ML) is the scientific study of algorithms and statistical models that PC systems use to play out a specific task without using unequivocal instructions, depending on patterns and deduction instead. There are various algorithms, which predict the heart disease. Accuracy is a key parameter to judge the algorithm. This paper proposed the efficient machine learning technique based on random forest. Accuracy, classification error, F-measure, Recall and Precision parameters are calculated. 92% accuracy achieved by proposed algorithm.

Key Words

Machine learning, F-measure, Recall and Precision.

Cite This Article

"An Efficient Machine Learning Technique for Prediction of Cardiac Diseases using ECG Dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.g378-g383, October-2023, Available :http://www.jetir.org/papers/JETIR2310544.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"An Efficient Machine Learning Technique for Prediction of Cardiac Diseases using ECG Dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. ppg378-g383, October-2023, Available at : http://www.jetir.org/papers/JETIR2310544.pdf

Publication Details

Published Paper ID: JETIR2310544
Registration ID: 526841
Published In: Volume 10 | Issue 10 | Year October-2023
DOI (Digital Object Identifier):
Page No: g378-g383
Country: Bhopal, MP, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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