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Feature selection and prediction of heart disease using machine learning approaches

Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis o...

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Main Authors: Molla, M. M.Imran, Islam, Md Sakirul, Shafi, A. S.M., Alam, Mohammad Khurshed, Islam, Md Tarequl, Jui, Julakha Jahan
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39523/1/Feature%20Selection%20and%20Prediction%20of%20Heart%20Disease%20Using%20Machine.pdf
http://umpir.ump.edu.my/id/eprint/39523/2/Feature%20selection%20and%20prediction%20of%20heart%20disease%20using%20machine%20learning%20approaches_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39523/
https://doi.org/10.1007/978-981-16-8690-0_83
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spelling my.ump.umpir.395232023-12-06T02:55:24Z http://umpir.ump.edu.my/id/eprint/39523/ Feature selection and prediction of heart disease using machine learning approaches Molla, M. M.Imran Islam, Md Sakirul Shafi, A. S.M. Alam, Mohammad Khurshed Islam, Md Tarequl Jui, Julakha Jahan T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis of cardiac disease has been considered via conventional medical history. Non-invasive approaches like machine learning are effective and powerful to categorize healthy people and people with heart disease. In the proposed research, by using the cardiovascular disease dataset, we created a machine-learning model to predict cardiac disease. In this paper, it is capable of recognizing and classifying the heart disease patient from healthy people by using three standard machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In addition, the Area Under Curve (AUC) value is calculated for each classification algorithms. In the proposed scheme, we also used the feature selection algorithm to reduce dimensions over a qualified heart disease dataset. After that, the whole structure for the classification of heart disease has been created. On complete features and reduced features, the performance of the proposed approach has been verified. The decrease in features affects the accuracy and time of execution of the classifiers. With the selected features, the highest classification accuracy is obtained for the KNN algorithm is about 93%, with a sensitivity is 0.9750 and specificity is 0.8529. Therefore, with the complete features, the classification accuracy is about 91%. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39523/1/Feature%20Selection%20and%20Prediction%20of%20Heart%20Disease%20Using%20Machine.pdf pdf en http://umpir.ump.edu.my/id/eprint/39523/2/Feature%20selection%20and%20prediction%20of%20heart%20disease%20using%20machine%20learning%20approaches_ABS.pdf Molla, M. M.Imran and Islam, Md Sakirul and Shafi, A. S.M. and Alam, Mohammad Khurshed and Islam, Md Tarequl and Jui, Julakha Jahan (2022) Feature selection and prediction of heart disease using machine learning approaches. In: Lecture Notes in Electrical Engineering; 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021, 23 August 2021 , Kuantan, Pahang. pp. 951-963., 842 (274719). ISSN 1876-1100 ISBN 978-981168689-4 https://doi.org/10.1007/978-981-16-8690-0_83
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Molla, M. M.Imran
Islam, Md Sakirul
Shafi, A. S.M.
Alam, Mohammad Khurshed
Islam, Md Tarequl
Jui, Julakha Jahan
Feature selection and prediction of heart disease using machine learning approaches
description Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis of cardiac disease has been considered via conventional medical history. Non-invasive approaches like machine learning are effective and powerful to categorize healthy people and people with heart disease. In the proposed research, by using the cardiovascular disease dataset, we created a machine-learning model to predict cardiac disease. In this paper, it is capable of recognizing and classifying the heart disease patient from healthy people by using three standard machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In addition, the Area Under Curve (AUC) value is calculated for each classification algorithms. In the proposed scheme, we also used the feature selection algorithm to reduce dimensions over a qualified heart disease dataset. After that, the whole structure for the classification of heart disease has been created. On complete features and reduced features, the performance of the proposed approach has been verified. The decrease in features affects the accuracy and time of execution of the classifiers. With the selected features, the highest classification accuracy is obtained for the KNN algorithm is about 93%, with a sensitivity is 0.9750 and specificity is 0.8529. Therefore, with the complete features, the classification accuracy is about 91%.
format Conference or Workshop Item
author Molla, M. M.Imran
Islam, Md Sakirul
Shafi, A. S.M.
Alam, Mohammad Khurshed
Islam, Md Tarequl
Jui, Julakha Jahan
author_facet Molla, M. M.Imran
Islam, Md Sakirul
Shafi, A. S.M.
Alam, Mohammad Khurshed
Islam, Md Tarequl
Jui, Julakha Jahan
author_sort Molla, M. M.Imran
title Feature selection and prediction of heart disease using machine learning approaches
title_short Feature selection and prediction of heart disease using machine learning approaches
title_full Feature selection and prediction of heart disease using machine learning approaches
title_fullStr Feature selection and prediction of heart disease using machine learning approaches
title_full_unstemmed Feature selection and prediction of heart disease using machine learning approaches
title_sort feature selection and prediction of heart disease using machine learning approaches
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39523/1/Feature%20Selection%20and%20Prediction%20of%20Heart%20Disease%20Using%20Machine.pdf
http://umpir.ump.edu.my/id/eprint/39523/2/Feature%20selection%20and%20prediction%20of%20heart%20disease%20using%20machine%20learning%20approaches_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39523/
https://doi.org/10.1007/978-981-16-8690-0_83
_version_ 1822923910549602304
score 13.232414

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