Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest
Stroke is a leading cause of global death and disability. This study proposes a stroke risk classification model using ensemble learning that combines Random Forest and XGBoost algorithms. A Kaggle dataset with 5110 samples (249 stroke, 4861 non-stroke) presented significant class imbalance. To addr...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9528 |
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