Heart Disease Prediction Using Machine Learning

Taher M. Ghazal; Amer Ibrahim; Ali Sheraz Akram; Zahid Hussain Qaisar; Sundus Munir; Shanza Islam

doi:10.1109/ICBATS57792.2023.10111368

Heart Disease Prediction Using Machine Learning

Taher M. Ghazal
, Amer Ibrahim
, Ali Sheraz Akram
, Zahid Hussain Qaisar
, Sundus Munir
, Shanza Islam

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

26 Citations (Scopus)

Abstract

The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc.

Original language	English
Title of host publication	2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350335644
DOIs	https://doi.org/10.1109/ICBATS57792.2023.10111368
Publication status	Published - 2023
Externally published	Yes
Event	2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 - Dubai, United Arab Emirates Duration: Mar 7 2023 → Mar 8 2023

Publication series

Name	2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

Conference

Conference	2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	3/7/23 → 3/8/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Artificial intelligence
Cardiovascular disease (CVD)
Machine Learning (ML)
Support Vector Machine (SVM)

ASJC Scopus subject areas

Management of Technology and Innovation
Artificial Intelligence
Computer Science Applications
Information Systems and Management
Management Science and Operations Research
Statistics, Probability and Uncertainty
Safety, Risk, Reliability and Quality
Health Informatics

Access to Document

10.1109/ICBATS57792.2023.10111368

Cite this

Ghazal, T. M., Ibrahim, A., Akram, A. S., Qaisar, Z. H., Munir, S., & Islam, S. (2023). Heart Disease Prediction Using Machine Learning. In 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 (2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBATS57792.2023.10111368

Heart Disease Prediction Using Machine Learning. / Ghazal, Taher M.; Ibrahim, Amer; Akram, Ali Sheraz et al.
2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023).

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

Ghazal, TM, Ibrahim, A, Akram, AS, Qaisar, ZH, Munir, S & Islam, S 2023, Heart Disease Prediction Using Machine Learning. in 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023. 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023, Dubai, United Arab Emirates, 3/7/23. https://doi.org/10.1109/ICBATS57792.2023.10111368

Ghazal TM, Ibrahim A, Akram AS, Qaisar ZH, Munir S, Islam S. Heart Disease Prediction Using Machine Learning. In 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023). doi: 10.1109/ICBATS57792.2023.10111368

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title = "Heart Disease Prediction Using Machine Learning",

abstract = "The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc.",

keywords = "Artificial intelligence, Cardiovascular disease (CVD), Machine Learning (ML), Support Vector Machine (SVM)",

author = "Ghazal, \{Taher M.\} and Amer Ibrahim and Akram, \{Ali Sheraz\} and Qaisar, \{Zahid Hussain\} and Sundus Munir and Shanza Islam",

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year = "2023",

doi = "10.1109/ICBATS57792.2023.10111368",

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TY - GEN

T1 - Heart Disease Prediction Using Machine Learning

AU - Ghazal, Taher M.

AU - Ibrahim, Amer

AU - Akram, Ali Sheraz

AU - Qaisar, Zahid Hussain

AU - Munir, Sundus

AU - Islam, Shanza

PY - 2023

Y1 - 2023

N2 - The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc.

AB - The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc.

KW - Artificial intelligence

KW - Cardiovascular disease (CVD)

KW - Machine Learning (ML)

KW - Support Vector Machine (SVM)

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DO - 10.1109/ICBATS57792.2023.10111368

M3 - Conference contribution

AN - SCOPUS:85160742243

T3 - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

BT - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

Y2 - 7 March 2023 through 8 March 2023

ER -

Heart Disease Prediction Using Machine Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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