Parkinson disease analysis using supervised and unsupervised techniques

Parkinson’s disease is classified as a disease of neurological origin, which is degenerative and chronic. Currently, the number of people affected by this disease has increased, one in 100 people over 60 years old, although it has been shown that the onset of this disease is approximately 60 years of age. Cases have also been identified of this disorder in patients as young as 18 years old suffer from this disease. Many tests have been developed throughout the literary review in order to identify patients tending to suffer from this disease that currently massifies its prevalence in the world. This article shows the implementation of different machine learning techniques such as LWL, ThresholdSelector, Kstar, VotedPercepton, CVParameterSelection, based on a test performed on experimental individuals and controls in order to identify the presence of the disease.

• Autores: Ariza Colpas, Paola Patricia; Morales Ortega, Roberto; Piñeres Melo, Marlon Alberto; De-La-Hoz-Franco, Emiro; Echeverri Ocampo, Isabel Cristina; Salas-Navarro, Katherinne
• Tipo de recurso: http://purl.org/coar/resource_type/c_816b
• Fecha de publicación: 2019
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/5603
• Acceso en línea: https://hdl.handle.net/11323/5603; https://repositorio.cuc.edu.co/
• Palabra clave: Parkinson’s disease; Neurodegenerative análisis; Spiral drawings; Machine learning approach
• Rights: openAccess
• License: CC0 1.0 Universal