Deep Learning Based Early Detection of Parkinson’s Disease Using Voice Signal Analysis

Parkinson’s disease (PD) is a progressive neurological disorder affecting more than eight million people worldwide. Early diagnosis is difficult because conventional clinical examinations usually detect the disease only after noticeable motor symptoms appear. Voice impairment is one of the earliest symptoms of Parkinson’s disease and can be used as a non‑invasive biomarker for early screening. This research proposes a deep learning‑based voice signal analysis system for early detection and classification of Parkinson’s disease. Voice recordings are preprocessed and acoustic features such as Mel Frequency Cepstral Coefficients (MFCC), jitter, shimmer, pitch and spectral parameters are extracted. Multiple deep learning architectures including Convolutional Neural Networks (CNN), Long Short‑Term Memory (LSTM) and hybrid CNN–LSTM models are implemented and evaluated. Experimental analysis shows that deep learning models can effectively detect abnormal voice characteristics related to Parkinson’s disease with high accuracy. The proposed system provides a cost‑effective and automated screening approach that can assist clinicians in early diagnosis and remote monitoring of patients.