Published: 2025-12-01
Application of Random Forest Method in Predicting Chronic Obstructive Pulmonary Disease (COPD)
DOI: 10.35870/ijsecs.v5i3.5551
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Abstract
Chronic Obstructive Pulmonary Disease (COPD) is one of the major global health problems and remains among the leading causes of death worldwide. Early detection plays a crucial role in preventing disease progression; however, conventional diagnostic methods such as spirometry and CT scans often require high costs, long processing time, and specialized expertise. This study aims to apply the Random Forest algorithm, one of the machine learning methods, to predict COPD based on clinical and lifestyle data. The dataset was obtained from Kaggle, consisting of attributes including age, gender, smoking status, type of occupation, sleep habits, exercise activity, insurance ownership, and history of comorbidities. The research stages include data preprocessing, train-test splitting (80:20), and model evaluation using accuracy, precision, recall, F1-score, and AUC metrics. The Random Forest model achieved an accuracy below 90% (approximately 87%), reflecting realistic performance in medical prediction while avoiding overfitting. The results indicate that Random Forest can serve as a reliable method for COPD detection and holds potential to be developed as the foundation of a Clinical Decision Support System (CDSS). This study contributes to the growing body of literature on the application of machine learning in healthcare, while also offering a faster, cost-effective, and scalable alternative for diagnosis.
Keywords
Chronic Obstructive Pulmonary Disease (COPD) ; Random Forest ; Machine Learning ; Medical Prediction ; Clinical Decision Support System
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 3 (2025)
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Section: Articles
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/ijsecs.v5i3.5551
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Muhhamad Fatkhurridlo Mahendra
Universitas Islam Nahdlatul Ulama Jepara, Jepara Regency, Central Java Province, Indonesia
Nur Aeni Widiyastuti
Universitas Islam Nahdlatul Ulama Jepara, Jepara Regency, Central Java Province, Indonesia
Sarwido Sarwido
Universitas Islam Nahdlatul Ulama Jepara, Jepara Regency, Central Java Province, Indonesia
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