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Biomarkers derived from human voice can offer in-sight into neurological disorders, such as Parkinson’s disease (PD), because of their underlying cognitive and neuromuscular function. Historically, PD has been difficult to quantity and doctors have tended to focus on some symptoms while ignoring others, relying primarily on subjective rating scales. With advancements in technology and the prevalence of audio collecting devices in daily lives, reliable models that can translate this audio data into a diagnostic tool for healthcare professionals would potentially provide diagnoses that are cheaper and more accurate. This project provides evidence to validate this concept here using a voice dataset collected from people with and without PD. This paper explores the effectiveness of using extreme gradient boosting algorithms, such as XG boost algorithm, to accurately diagnose individuals with the disease Researchers …