Standard audiogram classification from loudness scaling data using unsupervised, supervised, and explainable machine learning techniques

The findings demonstrate that ML models can predict standard Bisgaard audiogram types-within certain limits-from calibration-independent loudness perception data. This approach may support future hearing aid fitting in remote or resource-limited settings without requiring a traditional audiogram.