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Parkinson's disease (PD) has been found to affect 1 out of every 1000 people,being more inclined towards the population above 60 years. Leveragingwearable-systems to find accurate biomarkers for diagnosis has become the needof the hour, especially for a neurodegenerative condition like Parkinson's.This work aims at focusing on early-occurring, common symptoms, such as motorand gait related parameters to arrive at a quantitative analysis on thefeasibility of an economical and a robust wearable device. A subset of theParkinson's Progression Markers Initiative (PPMI), PPMI Gait dataset has beenutilised for feature-selection after a thorough analysis with various MachineLearning algorithms. Identified influential features has then been used to testreal-time data for early detection of Parkinson Syndrome, with a model accuracyof 91.9%

Early Detection of Parkinson's Disease using Motor Symptoms and Machine Learning