Analysis of Machine Learning Models for Heart Disease Prediction using Different Algorithms: A Review

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Published: Nov 15, 2022
DOI: https://doi.org/10.17762/ijfrcsce.v8i4.2114
Keywords:
Machine Learning, Cardiac Arrest, Neural Network, SVM, Logistic Regression

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Neeraj
NCCE Israna, Department of ECE, Panipat, Haryana, India
Jagtar Singh
NCCE Israna, Department of ECE, Panipat, Haryana, India

Abstract

Now a days the heart diseases are growing very rapidly making it an important and apprehensive task of prediction of these kinds of diseases in advance. The diagnosis is also a tough chore because it has to be performed in a precise and efficient manner. The emerging technology in modern life style integrated with internet of thing which having sensors and huge amount of data is sent to various clouds for further investigation using different algorithms to fetch out precise information for various domains. Across the world approximately 3 quintillion bytes/day information generated and this data stored for further examination. As data is in huge quantity therefore, appropriate methods applied to examine the perfect analysis so that prediction can be carried out optimally. Clinical decision making is dominant to all patient care happenings which includes choosing a deed, between replacements. These days emerging field like Machine Learning play prime role in healthcare to analyze and predict the diseases. After investigating numerous research article on Machine Learning, it was found that for same data set accuracy was different for various algorithms. In our research work different machine learning techniques will be implemented and will be tested for various parameters like accuracy, precision, recall on validated dataset. ML and Neural Networks are more capable in supporting deciding and predicting from the enormous data formed by health care systems.

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How to Cite
Neeraj, & Jagtar Singh. (2022). Analysis of Machine Learning Models for Heart Disease Prediction using Different Algorithms: A Review. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(4), 26–32. https://doi.org/10.17762/ijfrcsce.v8i4.2114
Issue
Vol. 8 No. 4 (2022)
Section
Articles
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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

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