Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost

This study aims to develop a heart disease classification model using an ensemble approach by leveraging a Stacking framework that combines BiGRU, BiLSTM, and XGBoost models. In this research, the BiGRU and BiLSTM models are utilized as base models to extract temporal and spatial features from se...

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Main Authors: Haldi, Budiman, Silvia, Ratna, M., Muflih, Usman, Syapotro, Muhammad, Hamdani, M.Rezqy, Noor Ridha
Format: Article
Language:English
English
Published: INTI International University 2024
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Online Access:http://eprints.intimal.edu.my/2053/1/jods2024_54.pdf
http://eprints.intimal.edu.my/2053/2/594
http://eprints.intimal.edu.my/2053/
http://ipublishing.intimal.edu.my/jods.html
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spelling my-inti-eprints.20532024-11-26T06:49:06Z http://eprints.intimal.edu.my/2053/ Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost Haldi, Budiman Silvia, Ratna M., Muflih Usman, Syapotro Muhammad, Hamdani M.Rezqy, Noor Ridha QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology (General) This study aims to develop a heart disease classification model using an ensemble approach by leveraging a Stacking framework that combines BiGRU, BiLSTM, and XGBoost models. In this research, the BiGRU and BiLSTM models are utilized as base models to extract temporal and spatial features from sequential data, while XGBoost is employed as a metamodel to perform the final classification based on the features generated by the two base models. The test results show that the BiGRU model achieves an accuracy of 0.77, while the BiLSTM model achieves an accuracy of 0.85. By applying the Stacking technique using XGBoost as the meta-model, the classification accuracy significantly increases to 0.92. These findings indicate that the Stacking framework can effectively enhance heart disease classification performance, making it a potentially powerful tool for medical applications in heart disease diagnosis. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2053/1/jods2024_54.pdf text en cc_by_4 http://eprints.intimal.edu.my/2053/2/594 Haldi, Budiman and Silvia, Ratna and M., Muflih and Usman, Syapotro and Muhammad, Hamdani and M.Rezqy, Noor Ridha (2024) Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost. Journal of Data Science, 2024 (54). pp. 1-5. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
T Technology (General)
Haldi, Budiman
Silvia, Ratna
M., Muflih
Usman, Syapotro
Muhammad, Hamdani
M.Rezqy, Noor Ridha
Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost
description This study aims to develop a heart disease classification model using an ensemble approach by leveraging a Stacking framework that combines BiGRU, BiLSTM, and XGBoost models. In this research, the BiGRU and BiLSTM models are utilized as base models to extract temporal and spatial features from sequential data, while XGBoost is employed as a metamodel to perform the final classification based on the features generated by the two base models. The test results show that the BiGRU model achieves an accuracy of 0.77, while the BiLSTM model achieves an accuracy of 0.85. By applying the Stacking technique using XGBoost as the meta-model, the classification accuracy significantly increases to 0.92. These findings indicate that the Stacking framework can effectively enhance heart disease classification performance, making it a potentially powerful tool for medical applications in heart disease diagnosis.
format Article
author Haldi, Budiman
Silvia, Ratna
M., Muflih
Usman, Syapotro
Muhammad, Hamdani
M.Rezqy, Noor Ridha
author_facet Haldi, Budiman
Silvia, Ratna
M., Muflih
Usman, Syapotro
Muhammad, Hamdani
M.Rezqy, Noor Ridha
author_sort Haldi, Budiman
title Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost
title_short Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost
title_full Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost
title_fullStr Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost
title_full_unstemmed Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost
title_sort classification of heart disease using a stacking framework of bigru, bilstm, and xgboost
publisher INTI International University
publishDate 2024
url http://eprints.intimal.edu.my/2053/1/jods2024_54.pdf
http://eprints.intimal.edu.my/2053/2/594
http://eprints.intimal.edu.my/2053/
http://ipublishing.intimal.edu.my/jods.html
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score 13.222552