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Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method

Coronary heart disease has been ranked as the number one leading cause of death in Malaysia. Based on the recent data published by WHO in 2018, death caused by this disease has reached 34,766 which brought up to 24.69 of the total deaths and places the Malaysian population 64th in the world. Medical...

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Main Authors:	Mohd Syafiq Asyraf, Suhaimi, Nor Azuana, Ramli, Noryanti, Muhammad
Format:	Article
Language:	English English
Published:	AIP Publishing 2024
Subjects:	QA75 Electronic computers. Computer science RC Internal medicine
Online Access:	http://umpir.ump.edu.my/id/eprint/41052/1/syafiqicoaims.pdf http://umpir.ump.edu.my/id/eprint/41052/7/Heart%20disease%20prediction%20using%20ensemble%20of%20k-nearest_ABST.pdf http://umpir.ump.edu.my/id/eprint/41052/ https://doi.org/10.1063/5.0192203 https://doi.org/10.1063/5.0192203
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id	my.ump.umpir.41052
record_format	eprints
spelling	my.ump.umpir.410522024-04-24T04:20:56Z http://umpir.ump.edu.my/id/eprint/41052/ Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Noryanti, Muhammad QA75 Electronic computers. Computer science RC Internal medicine Coronary heart disease has been ranked as the number one leading cause of death in Malaysia. Based on the recent data published by WHO in 2018, death caused by this disease has reached 34,766 which brought up to 24.69 of the total deaths and places the Malaysian population 64th in the world. Medical researchers all around the world believe that there are multiple circumstances for this disease which include health problems, unhealthy personal habits, genetics, and family history. It is not an easy task to predict heart disease since the study needs a broad range of expertise from many disciplines. Recently, machine learning had been applied as one of the methods to predict heart disease. To test the accuracy of different machine learning methods, this study is conducted by applying the data extracted from the machine learning repository. The proposed predictive modelling in this study was developed using the ensemble method. The ensemble technique used was stacking where logistic regression was used as the meta-level classifier while Random Forest and k-nearest neighbour method were applied as the meta-level classifiers. Results obtained from this study show that the proposed method outperforms other single methods with 82.42 accuracies. Although the accuracy and RMSE of the ensemble method are similar to Random Forest, the proposed method is still the best method since it has a 0.903 value for the area under the ROC and 0.843 value for F1 score. This proposed predictive model will be applied by using smartwatch datasets for future study. AIP Publishing 2024-03-07 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41052/1/syafiqicoaims.pdf pdf en http://umpir.ump.edu.my/id/eprint/41052/7/Heart%20disease%20prediction%20using%20ensemble%20of%20k-nearest_ABST.pdf Mohd Syafiq Asyraf, Suhaimi and Nor Azuana, Ramli and Noryanti, Muhammad (2024) Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method. AIP Conference Proceedings, 2895 (1). pp. 1-10. ISSN 0094-243X. (Published) https://doi.org/10.1063/5.0192203 https://doi.org/10.1063/5.0192203
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	QA75 Electronic computers. Computer science RC Internal medicine
spellingShingle	QA75 Electronic computers. Computer science RC Internal medicine Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Noryanti, Muhammad Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method
description	Coronary heart disease has been ranked as the number one leading cause of death in Malaysia. Based on the recent data published by WHO in 2018, death caused by this disease has reached 34,766 which brought up to 24.69 of the total deaths and places the Malaysian population 64th in the world. Medical researchers all around the world believe that there are multiple circumstances for this disease which include health problems, unhealthy personal habits, genetics, and family history. It is not an easy task to predict heart disease since the study needs a broad range of expertise from many disciplines. Recently, machine learning had been applied as one of the methods to predict heart disease. To test the accuracy of different machine learning methods, this study is conducted by applying the data extracted from the machine learning repository. The proposed predictive modelling in this study was developed using the ensemble method. The ensemble technique used was stacking where logistic regression was used as the meta-level classifier while Random Forest and k-nearest neighbour method were applied as the meta-level classifiers. Results obtained from this study show that the proposed method outperforms other single methods with 82.42 accuracies. Although the accuracy and RMSE of the ensemble method are similar to Random Forest, the proposed method is still the best method since it has a 0.903 value for the area under the ROC and 0.843 value for F1 score. This proposed predictive model will be applied by using smartwatch datasets for future study.
format	Article
author	Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Noryanti, Muhammad
author_facet	Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Noryanti, Muhammad
author_sort	Mohd Syafiq Asyraf, Suhaimi
title	Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method
title_short	Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method
title_full	Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method
title_fullStr	Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method
title_full_unstemmed	Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method
title_sort	heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method
publisher	AIP Publishing
publishDate	2024
url	http://umpir.ump.edu.my/id/eprint/41052/1/syafiqicoaims.pdf http://umpir.ump.edu.my/id/eprint/41052/7/Heart%20disease%20prediction%20using%20ensemble%20of%20k-nearest_ABST.pdf http://umpir.ump.edu.my/id/eprint/41052/ https://doi.org/10.1063/5.0192203 https://doi.org/10.1063/5.0192203
_version_	1822924290264137728
score	13.235362

Heart disease prediction using ensemble of k-nearest neighbour, random forest and logistic regression method

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