Parkinson's disease diagnosis: The effect of autoencoders on extracting features from vocal characteristics

Abstract

This paper aims to employ Machine Learning (ML) classifying algorithms to predict whether the patient has Parkinson's Disease (PD) or not. Motor disorders mainly characterize PD, and consequently, a variety of data sets are recorded from the motor system. These data sets consist of either physical behaviors of patients or neuroimaging data captured from their brains. However, the disease mostly begins years before the motor symptoms. Consequently, non-motor symptoms have been studied more in the last decade. Since about 90% of patients experience vocal disorders in the early stages, these symptoms can be more useful for diagnosing the disease. We will review data sets developed for PD diagnosis and some machine learning classification models applied to these data sets. We will offer some models to accurately predict PD according to vocal symptoms characteristics provided in the UCI Machine Learning database, which suffers a low number of samples compared to features and being imbalanced. The results of comparative studies demonstrate that the proposed classic classification models can outperform various Deep learning methods that have been previously used in the literature. The accuracy of 97.22% was obtained by using Logistic Regression and Voting algorithms.