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All Journal International Journal Software Engineering and Computer Science (IJSECS)
Nur Aeni Widiyastuti
Universitas Islam Nahdlatul Ulama Jepara
Computer Science & IT

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Application of Random Forest Method in Predicting Chronic Obstructive Pulmonary Disease (COPD) Muhhamad Fatkhurridlo Mahendra; Nur Aeni Widiyastuti; Sarwido Sarwido
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5551

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.
Co-Authors Muhhamad Fatkhurridlo Mahendra Sarwido Sarwido