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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2053/ http://eprints.intimal.edu.my/2053/1/jods2024_54.pdf http://eprints.intimal.edu.my/2053/2/594 |
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| 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 |
| building | INTI Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:58:36Z |
| format | Article |
| id | intimal-2053 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:36Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology (General) |
| url | http://eprints.intimal.edu.my/2053/ http://eprints.intimal.edu.my/2053/ http://eprints.intimal.edu.my/2053/1/jods2024_54.pdf http://eprints.intimal.edu.my/2053/2/594 |