Heart Disease Prediction Using Machine Learning Algorithms

Rea Mammen; Arti Pawar

doi:10.1007/978-981-19-6913-3_16

Heart Disease Prediction Using Machine Learning Algorithms

Rea Mammen
, Arti Pawar^*

^*Corresponding author for this work

International Centre for Applied Sciences, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Heart disease is synonymous with heart attacks and strokes. But, cardiovascular disease also includes maladies like coronary artery disease (CAD), heart arrhythmias, hypertension, congenital heart disease, etc. Heart disease plagues a majority of the population today and is the leading cause of death globally. Efficient prediction systems to diagnose heart diseases are a must in the health care industry. Such systems are already in use but there is scope for improvement and with technological advancement over the years, the accuracy of disease prediction has been improved. Machine learning is a branch of artificial intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze a few machine learning algorithms and tests their accuracy in predicting heart diseases.

Original language	English
Title of host publication	Smart Sensors Measurement and Instrumentation - Select Proceedings of CISCON 2021
Editors	Shreesha Chokkadi, Rajib Bandyopadhyay
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	239-253
Number of pages	15
ISBN (Print)	9789811969126
DOIs	https://doi.org/10.1007/978-981-19-6913-3_16
Publication status	Published - 2023
Event	Control Instrumentation System Conference, CISCON 2021 - Virtual, Online Duration: 26-11-2021 → 27-11-2021

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	957
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	Control Instrumentation System Conference, CISCON 2021
City	Virtual, Online
Period	26-11-21 → 27-11-21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-19-6913-3_16

Cite this

Mammen, R., & Pawar, A. (2023). Heart Disease Prediction Using Machine Learning Algorithms. In S. Chokkadi, & R. Bandyopadhyay (Eds.), Smart Sensors Measurement and Instrumentation - Select Proceedings of CISCON 2021 (pp. 239-253). (Lecture Notes in Electrical Engineering; Vol. 957). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-6913-3_16

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title = "Heart Disease Prediction Using Machine Learning Algorithms",

abstract = "Heart disease is synonymous with heart attacks and strokes. But, cardiovascular disease also includes maladies like coronary artery disease (CAD), heart arrhythmias, hypertension, congenital heart disease, etc. Heart disease plagues a majority of the population today and is the leading cause of death globally. Efficient prediction systems to diagnose heart diseases are a must in the health care industry. Such systems are already in use but there is scope for improvement and with technological advancement over the years, the accuracy of disease prediction has been improved. Machine learning is a branch of artificial intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze a few machine learning algorithms and tests their accuracy in predicting heart diseases.",

author = "Rea Mammen and Arti Pawar",

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year = "2023",

doi = "10.1007/978-981-19-6913-3\_16",

language = "English",

isbn = "9789811969126",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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address = "Germany",

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Mammen, R & Pawar, A 2023, Heart Disease Prediction Using Machine Learning Algorithms. in S Chokkadi & R Bandyopadhyay (eds), Smart Sensors Measurement and Instrumentation - Select Proceedings of CISCON 2021. Lecture Notes in Electrical Engineering, vol. 957, Springer Science and Business Media Deutschland GmbH, pp. 239-253, Control Instrumentation System Conference, CISCON 2021, Virtual, Online, 26-11-21. https://doi.org/10.1007/978-981-19-6913-3_16

Heart Disease Prediction Using Machine Learning Algorithms. / Mammen, Rea; Pawar, Arti.
Smart Sensors Measurement and Instrumentation - Select Proceedings of CISCON 2021. ed. / Shreesha Chokkadi; Rajib Bandyopadhyay. Springer Science and Business Media Deutschland GmbH, 2023. p. 239-253 (Lecture Notes in Electrical Engineering; Vol. 957).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Heart disease is synonymous with heart attacks and strokes. But, cardiovascular disease also includes maladies like coronary artery disease (CAD), heart arrhythmias, hypertension, congenital heart disease, etc. Heart disease plagues a majority of the population today and is the leading cause of death globally. Efficient prediction systems to diagnose heart diseases are a must in the health care industry. Such systems are already in use but there is scope for improvement and with technological advancement over the years, the accuracy of disease prediction has been improved. Machine learning is a branch of artificial intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze a few machine learning algorithms and tests their accuracy in predicting heart diseases.

AB - Heart disease is synonymous with heart attacks and strokes. But, cardiovascular disease also includes maladies like coronary artery disease (CAD), heart arrhythmias, hypertension, congenital heart disease, etc. Heart disease plagues a majority of the population today and is the leading cause of death globally. Efficient prediction systems to diagnose heart diseases are a must in the health care industry. Such systems are already in use but there is scope for improvement and with technological advancement over the years, the accuracy of disease prediction has been improved. Machine learning is a branch of artificial intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze a few machine learning algorithms and tests their accuracy in predicting heart diseases.

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Heart Disease Prediction Using Machine Learning Algorithms

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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