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Data-Driven Based Approach to Aid Parkinson’s Disease Diagnosis - PMC

pmc.ncbi.nlm.nih.gov · 8,854 words · saved by 1 readers

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Abstract This article presents a machine learning methodology for diagnosing Parkinson’s disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The…

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