Three powerful nonlinear statistical algorithms [a support vector machine (SVM), radial basis function network (RBFN), and regression tree] were used to build prediction models for noise-induced hearing loss (NIHL). The models were developed from an animal (chinchilla) database consisting of 322 animals exposed to 30 Gaussian and non-Gaussian noise conditions. The inputs for the models were either energy or energy plus kurtosis. The models predict inner hair cell (IHC) loss, outer hair cell (OHC) loss, and postexposure threshold shift (PTS) at 0.5, 1, 2, 4, and 8 kHz. The models incorporating both energy and kurtosis improved the prediction performance significantly. The average performance improvement for the prediction of IHC loss was as much as 55%, for OHC loss it was 66% and for PTS, 61%. The prediction accuracy of SVM and RBFN with energy plus kurtosis for all three outputs (predictions) was more than 90% while for the regression tree model it was more than 85%. Energy is not a sufficient metric to predict hearing trauma from complex (non- Gaussian) noise exposure. A kurtosis metric may be necessary for the prediction of NIHL.
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