Advanced Machine Learning Approaches for Multi-Class Lung Disease Classification Using Respiratory Acoustics

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This study introduces a novel method for the multi-class classification of lung diseases using respiratory sounds. Lung diseases represent a major global health issue, and early detection is essential for effective treatment and management. Traditional diagnostic techniques often involve specialized equipment and expertise, which can limit accessibility and increase costs. In this research, we propose a machine learning-based approach that utilizes respiratory sounds, which can be easily collected with affordable, non-invasive devices like digital stethoscopes or smartphone apps. Our method involves extracting features from respiratory sound signals and training machine learning models to classify various lung conditions, including pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and bronchiectasis. We validate our approach using realworld respiratory sound datasets and evaluate its performance based on accuracy, sensitivity, and specificity. The results highlight the potential of using respiratory sounds for multi-class lung disease classification, providing an effective solution for early diagnosis and remote monitoring, especially in resource-constrained environments. This research contributes to the evolution of digital healthcare technologies, offering significant potential for enhancing respiratory disease management and improving patient outcomes