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

arXiv: https://arxiv.org/abs/2304.09245 · HTML: https://mlsanity.com/paper/2304.09245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 18 Apr 2023.

## Abstract (arXiv)

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