A latent variable-based Bayesian regression to address recording replications in Parkinson's Disease

Subject-based approaches are proposed to automatically discriminate healthy people from those with Parkinson's Disease (PD) by using speech recordings. These approaches have been applied to one of the most used PD datasets, which contains repeated measurements in an imbalanced design. Most of the published methodologies applied to perform classification from this dataset fail to account for the dependent nature of the data. This fact artificially increases the sample size and leads to a diffuse criterion to define which subject is suffering from PD. The first proposed approach is based on data aggregation. This reduces the sample size, but defines a clear criterion to discriminate subjects. The second one handles repeated measurements by introducing latent variables in a Bayesian logistic regression framework. The proposed approaches are conceptually simple and easy to implement.