An Enhanced Voice Record Recognition System for Parkinson's Disease Progression using Deep Neural Networks

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disease that manifests itself with a variety of motor and non-motor symptoms. Many PD patients have difficulty moving in a normal manner in the early stages. One of the most common symptoms is vocal disorders. Recent PD detection studies have focused on diagnostic systems based on vocal disorders that hold a lot of promise as an excitingly new field of research. Deep Learning has grown in prominence in recent years for a variety of prediction issues that are challenging the medical professionals. In this paper, Back Propagation Deep Neural Networks (BPDNN) is applied with multiple architectures to create better predictive models for detection of Parkinson's disease (PD) based on the analysis of the features collected from different speech samples of patients. Significantly, even without the use of a feature selection method, Deep Neural Networks has emerged as the best classification tool for PD diagnosis. Finally, DNN was fine-tuned, resulting in a train precision of 99.35%.