Abstract
Abstract: This study proposed a novel anomaly-based detection system for Chronic Obstructive Pulmonary Disease (COPD) using machine learning techniques. The system was trained and tested on a dataset of respiratory patterns, vital signs, and other relevant features. The machine learning model achieved high accuracy and sensitivity, with an F1-score of 0.834, an ROC AUC of 0.921, and a precision of 0.781. The detected anomalies were found to be strongly correlated with COPD severity, suggesting that the proposed framework has potential clinical significance. The system shows promise in COPD detection, further research is needed to improve the system's generalizability across different populations, and to explore opportunities for real-world implementation. The study's findings can contribute to the development of more effective and efficient COPD management strategies, potentially leading to improved patient outcomes and reduced healthcare costs.
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