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A Comprehensive Review of Various Machine Learning Techniques for Heart Disease Prediction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Guna Sekhar Sajja

Guna Sekhar Sajja. A Comprehensive Review of Various Machine Learning Techniques for Heart Disease Prediction. International Journal of Computer Applications 183(37):53-56, November 2021. BibTeX

	author = {Guna Sekhar Sajja},
	title = {A Comprehensive Review of Various Machine Learning Techniques for Heart Disease Prediction},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2021},
	volume = {183},
	number = {37},
	month = {Nov},
	year = {2021},
	issn = {0975-8887},
	pages = {53-56},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2021921772},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Data mining techniques have been used by several researchers to detect illnesses. Some methods are intended to predict a single sickness, while others are intended to predict a wide variety of diseases. It is also possible to improve the accuracy of sickness prediction. In this post, we provided an overview of data classification approaches that are available. These algorithms are mostly represented by themselves. The classification of data is a common and computationally difficult procedure. We've also established the foundation for data categorization. We would compare the best algorithms from a huge set of existing algorithms. This article presents a summary of the research on machine learning and soft computing-based methods for categorizing and predicting cardiac disease.


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Data Mining, Machine Learning, Classification, Prediction, Heart Disease