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

• 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
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; RC0254 Neoplasms. Tumors. Oncology (including Cancer); T Technology (General)
• 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

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...