ARCHIVES

Chronic Obstructive Pulmonary Disease (COPD) is a long-term respiratory disorder that significantly contributes to global morbidity and mortality rates. Traditional diagnostic methods, such as spirometry and imaging, are often limited by accessibility, cost, and accuracy constraints. Machine learning (ML) presents innovative solutions for COPD management, including early diagnosis, severity prediction, exacerbation forecasting, and personalized treatment strategies. This review systematically explores ML applications in COPD, evaluating supervised learning classifiers, deep learning architectures, and reinforcement learning approaches while assessing their real-world applicability. This study explores critical challenges, including data quality issues, class imbalance, model interpretability, and difficulties in clinical integration. Furthermore, it emphasizes the significance of Explainable AI (XAI), multi-modal data fusion, and Internet of Things (IoT)-based real-time monitoring in improving COPD management. Future research should focus on hybrid Artificial Intelligence (AI) frameworks, federated learning for privacy-preserving model training, and seamless AI integration into clinical workflows. Addressing these gaps will enable ML-driven solutions to optimize COPD treatment, reduce hospitalizations, and improve patient outcomes.