Analysis of the Performance of Supervised Learning Algorithms in the Classification of Gait in Parkinsonian Patients

• Hugo A. Souza UFAL
• Marcelo Costa Oliveira UFAL
• Leonardo Melo de Medeiros IFAL

DOI: https://doi.org/10.5753/sbcas.2017.3733

Abstract

The gait analysis has become an attractive and non-invasive quantitative mechanism that can aid in the detection and monitoring of Parkinson's disease patients. The extraction of characteristics is a task of paramount importance for the quality of the data to be used by the algorithms of Machine Learning, aiming as main objective the reduction in the dimensionality of the data in a classification process. This work evaluates the performance of supervised learning algorithms in the classification of human gait characteristics in PD patients.

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