A detailed examination of  machine learning techniques for predicting heart illness

Priyanshu Singh; Prakruthi HR; Nayana Sagar; Moulya G; Thirukrishna JT

A detailed examination of machine learning techniques for predicting heart illness

May 2023

Vol-9, Issue-3

Paper ID: 20017

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Science

Keywords

Cardiovascular disorders decision trees logistic regression (LR) machine learning(ML) with support vector machines (SVM) KNN

Abstract

The prenatal recognition of CVDs can help high-risk patients choose whether to change the way they live , which can lessen their severity. Using consistent techniques of machine learning, research has sought to identify the most significant risk variables for heart disease as well as effectively estimate the total risk. In order to produce an accurate predictive algorithm for heart disease, the latest research has looked at bringing together these methods using techniques like machine learning (ml) algorithms. These findings recommend a framework for assessing the precision of implementing particular outcomes from the use of decision trees, k-near neighbor, and logistic regression and SVM on the Cleveland Heart Disease Database.

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Author Information

#	Name	Institute / Affiliation
1	Priyanshu Singh	Dayananda Sagar Academy of Tech & Management
2	Prakruthi HR	Dayananda Sagar Academy of Tech & Management
3	Nayana Sagar	Dayananda Sagar Academy of Tech & Management
4	Moulya G	Dayananda Sagar Academy of Tech & Management
5	Thirukrishna JT	Dayananda Sagar Academy of Tech & Management

How to Cite

Use the following formats to cite this article in your research.

APA Style

Singh, Priyanshu, HR, Prakruthi, Sagar, Nayana, G, Moulya, & JT, Thirukrishna (2023). A detailed examination of machine learning techniques for predicting heart illness. International Journal of Advance Research and Innovative Ideas In Education, 9(3), 488-497.

MLA Style

Singh, Priyanshu, et al. "A detailed examination of machine learning techniques for predicting heart illness." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 3, 2023, pp. 488-497.

IEEE Style

Priyanshu Singh, Prakruthi HR, Nayana Sagar, Moulya G, and Thirukrishna JT, "A detailed examination of machine learning techniques for predicting heart illness," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 3, pp. 488-497, 2023.

Vancouver Style

Singh Priyanshu, HR Prakruthi, Sagar Nayana, G Moulya, JT Thirukrishna. A detailed examination of machine learning techniques for predicting heart illness. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(3):488-497.

Harvard Style

Singh, Priyanshu, HR, Prakruthi, Sagar, Nayana, G, Moulya, & JT, Thirukrishna (2023) 'A detailed examination of machine learning techniques for predicting heart illness', International Journal of Advance Research and Innovative Ideas In Education, 9(3), pp. 488-497.

Chicago Style

Singh, Priyanshu, et al. "A detailed examination of machine learning techniques for predicting heart illness." International Journal of Advance Research and Innovative Ideas In Education 9, no. 3 (2023): 488-497.

Turabian Style

Singh, Priyanshu, et al. "A detailed examination of machine learning techniques for predicting heart illness." International Journal of Advance Research and Innovative Ideas In Education 9, no. 3 (2023): 488-497.