Developing a smart system for binary classification of disordered voices using machine learning

This study underscores the potential of machine learning, particularly the RF algorithm, in enhancing the accuracy of voice disorder diagnosis by automating acoustic feature analysis. The integration of such models into clinical practice could offer more reliable, non-invasive methods for early detection and management of voice disorders, thus improving patient outcomes. Future research should focus on expanding dataset diversity and further validation to enhance the generalizability and clinical applicability of these findings.