Machine Learning Techniques in Predicting Heart Disease a Survey

Title

Machine Learning Techniques in Predicting Heart Disease a Survey

Creator

Roy R.E.; Kulkarni P.; Kumar S.

Description

The heart serves an important role in living creatures. Diagnosis and forecast of cardiac illnesses demand greater precision, perfection, and accuracy because such tiny mistakes can lead to weariness and death. Numerous heart-related deaths have occurred, and the incidence rates have been rising over time. Predicting the development of heart disorders is important to work in the medical industry. Every month, many databases related to the patient are kept. The information gathered can be used to predict the occurrence of future diseases. This article gives an outline of cardiovascular diseases and modern treatments. Also, the focus of this research is to outline some current research on applying machine learning techniques to predict heart disease, analyze the many machine learning algorithms employed, and determine which technique(s) are useful and efficient. Artificial neural network (ANN), decision tree (DT), fuzzy logic, K-nearest neighbor (KNN), Naive bayes (NB), and support vector machine (SVM) are data mining and machine learning approaches used to predict cardiac disease. This paper includes an overview of the present method based on features, the algorithms are compared, and the most accurate algorithm is analyzed. 2022 IEEE.

Source

Proceedings - 2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022, pp. 373-377.

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

and random forest; decision tree; heart disease; logistic regression; support vector machine

Coverage

Roy R.E., Christ (Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, 560074, India; Kulkarni P., Christ (Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, 560074, India; Kumar S., Christ (Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, 560074, India

Rights

Restricted Access

Relation

ISBN: 978-150905001-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/AIC55036.2022.9848945

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