Development of the best ensemble-based machine learning classifier for distinguishing hypokinetic dysarthria caused by Parkinson's disease from presbyphonia and comparison of performance measures
H. Byeon
Full text PDF 
( Download count: 4090)
AbstractWhen people get old, they experience vocal aging due to the malfunction of the respiratory system and the vocal system. It is defined as presbyphonia in otorhinolaryngology. Presbyphonia generally shows symptoms such as hoarse, weak, or trembling voice due to the atrophy or loss of elasticity of the vocal cord muscles in the aging process. These symptoms are similar to the major vocal symptoms of early Parkinson’s disease that are caused by damage to the nervous system. However, presbyphonia can be distinguished from neurological voice disorders such as vocal cord paralysis because presbyphonia is not a voice disorder. Therefore, it is essential to understand the aging process of voice characteristics to accurately distinguish presbyphonia from neurological voice disorders. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree, random forest, GBM, and XGBoost, and compared the prediction performance of models.Keywords: presbyphonia, Parkinson's disease, Hypokinetic dysarthria, ceptral, spectral
H. Byeon (2022). Development of the best ensemble-based machine learning classifier for distinguishing hypokinetic dysarthria caused by Parkinson's disease from presbyphonia and comparison of performance measures. Gerontechnology, 21(s),1-1
https://doi.org/10.4017/gt.2022.21.s.612.pp3