Voice-Based Detection of Parkinson's Disease Using Machine and Deep Learning Approaches: A Systematic Review.

ABSTRACT: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, among which vocal impairment is one of the earliest and most prevalent. In recent years, voice analysis supported by machine learning (ML) and deep learning (DL) has emerged as a promising non-invasive method for early PD detection. We conducted a systematic review searching PubMed, Scopus, IEEE Xplore, and Web of Science databases for studies published between 2020 and September 2025. A total of 69 studies met the inclusion criteria and were analyzed in terms of dataset characteristics, speech tasks, feature extraction techniques, model architectures, validation strategies, and performance outcomes. Classical ML models such as Support Vector Machines (SVMs) and Random Forest

SUBMITTER: Sedigh Malekroodi H 

PROVIDER: S-EPMC12649940 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature