RL Based COPD Diagnostic Framework

Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory condition that challenges early detection and management. Traditional diagnostic methods are often time-consuming and resource-intensive. Recent advancements in artificial intelligence offer new possibilities for improving diagnostic accuracy. This paper explores using reinforcement learning (RL) for automated COPD detection. We introduce a novel RL-based framework that uses patient data—such as symptoms, medical history, and imaging results—to train an agent to differentiate between COPD and non-COPD cases. By employing a reward-based learning mechanism, our approach enhances decision-making and diagnostic performance. Experimental results show that our RL model offers higher accuracy and faster processing times than conventional methods, highlighting its potential for revolutionizing COPD diagnosis