Predicting Respiratory Conditions Using Random Forest and XGBoost

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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Bibliographic Details
Main Authors: Dhiyaussalam Dhiyaussalam, Ahmad Yusuf, Isna Wardiah, Nitami Lestari Putri
Format: Article
Language:English
Published: Informatics Department, Faculty of Computer Science Bina Darma University 2025-06-01
Series:Journal of Information Systems and Informatics
Subjects:
machine learning
random forest
respiratory disease
severity classification
xgboost
Online Access:https://journal-isi.org/index.php/isi/article/view/1124
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