Parkinson Disease Analysis Using Supervised and Unsupervised Techniques

• Published in: Advances in Swarm Intelligence Vol. 11656; pp. 191 - 199
• Main Authors: Ariza-Colpas, Paola, Morales-Ortega, Roberto, Piñeres-Melo, Marlon, De la Hoz-Franco, Emiro, Echeverri-Ocampo, Isabel, Salas-Navarro, Katherinne
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Machine learning approach; Neurodegenerative analysis; Parkinson’s disease; Spiral drawings
• ISBN: 3030263533; 9783030263539
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-26354-6_19

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.