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: | , , , , , |
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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/2053/1/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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Summary: | 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. |
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