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

• URL: http://arxiv.org/abs/2304.09245v1
• Date: Tue, 18 Apr 2023 19:13:05 GMT
• Title: Early Detection of Parkinson's Disease using Motor Symptoms and Machine Learning
• Authors: Poojaa C and John Sahaya Rani Alex
• Abstract summary: 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. 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%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%

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