Abstract: Parkinson’s disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their inability to produce dopamine, an organic chemical responsible for controlling a person’s movement. Diagnosis involves many physical and psychological tests and specialist examinations of the patient’s nervous system, which causes several issues. The PD Speech data-set used in this experiment exhibits huge dimensionality with comparatively less data-points. Random Forest classifier is used to classify individuals as either PD or healthy control (HC). The proposed system offers a non-invasive, cost-effective, and accessible approach to PD diagnosis, potentially improving healthcare outcomes and patient quality of life. This method extracts a set of features from a recording of the person’s voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson’s cases.  This project aims to promote the integration of voice analysis into routine clinical practice for early PD detection and intervention.

Keywords: Parkinson's Disease, Machine Learning, Audio Analysis, Random Forest, Diagnosis.

Cite:
Sailakshmi Lakkakula, D. LakshmiSaranya, J. Swathi, A.V.A.N. Sarvani,"Detecting Parkinson’s Disease Through Voice Analysis ", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 11, no. 3, 2024, Crossref https://doi.org/10.17148/IARJSET.2024.11315.

| DOI: 10.17148/IARJSET.2024.11315