Predicting COPD Risk Using Machine Learning Algorithms

Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung condition that affects quality of life and contributes to high global mortality. Early detection is vital but often hindered by complex, traditional diagnostic methods. This project presents a non-invasive COPD prediction system using sound analysis and machine learning. By examining respiratory sounds like coughs and wheezes from simple audio recordings, the system extracts key acoustic features to predict COPD risk. Developed as a user friendly web tool, it allows real-time predictions from uploaded recordings, making it a cost-effective and scalable solution especially in remote or low-resource areas. The model also offers interpretability, supporting informed clinical decisions and proactive care. .