Predicting Respiratory Conditions Using Random Forest and XGBoost
This study examines the performance of Random Forest and XGBoost in predicting the diagnosis and severity of respiratory diseases using a simulated dataset of 2,000 patient records. The models were tested on two classification tasks: identifying disease types (e.g., pneumonia, influenza) and classif...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Informatics Department, Faculty of Computer Science Bina Darma University
2025-06-01
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| Series: | Journal of Information Systems and Informatics |
| Subjects: | |
| Online Access: | https://journal-isi.org/index.php/isi/article/view/1124 |
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