Parkinson's Disease Detection on Unbalanced Speech Data using Convolutional Neural Networks

Abstract

Parkinson’s disease is a progressive condition impacting movement and communication. Initially, symptoms manifest primarily in speech difficulties, which worsen over time, affecting aspects such as pitch and articulation. Detecting signs of Parkinson's disease often relies on analysing speech patterns. In this study, a Convolutional Neural Network is utilized for Parkinson's speech detection. Convolutional Neural Network excels in capturing subtle spatial structures and local patterns, crucial for discerning the nuanced pitch, rhythm, and phonetic traits of individuals with Parkinson’s disease. The research employs acoustic voice measures like jitter and shimmer as speech input parameters, utilizing a dataset sourced from the UCI machine learning repository. The dataset exhibits a class imbalance problem. To address the class imbalance issue Synthetic Minority Over-sampling Technique algorithm is used. The Convolutional Neural Network algorithm significantly enhances Parkinson’s disease voice detection exhibiting a remarkable accuracy of 91.52% outperforming traditional machine learning approaches.