Detection of Parkinson’s Disease Using Voice Analysis and Deep Learning

Parkinson’s disease (PD) is a progressive neurological disorder that affects millions of people worldwide and is usually diagnosed at later stages due to reliance on clinical observation and subjective assessment. Early detection is essential for improving treatment outcomes and quality of life. Voice impairment is considered one of the earliest and non-invasive biomarkers of Parkinson’s disease. This study investigates the effectiveness of deep learning techniques for detecting Parkinson’s disease using voice signal analysis. The proposed approach utilizes speech recordings and extracts acoustic features such as jitter, shimmer, harmonics-to-noise ratio (HNR), and Mel Frequency Cepstral Coefficients (MFCC). Deep learning architectures including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are used to classify voice signals and identify Parkinsonian speech patterns. The analysis of existing research indicates that deep learning models outperform traditional machine learning approaches and provide improved accuracy in detecting early symptoms of Parkinson’s disease. The proposed approach offers a non-invasive, cost-effective, and automated solution for early diagnosis and remote health monitoring applications