Parkinson Disease Analysis Using Supervised and Unsupervised Techniques
| Autor: | Paola Ariza-Colpas, Emiro De la Hoz-Franco, Isabel Echeverri-Ocampo, Katherinne Salas-Navarro, Roberto Cesar Morales-Ortega, Marlon Alberto Piñeres-Melo |
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| Rok vydání: | 2019 |
| Předmět: |
Pediatrics
medicine.medical_specialty Parkinson's disease business.industry 020208 electrical & electronic engineering 02 engineering and technology Disease medicine.disease Test (assessment) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing In patient business |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030263539 ICSI (2) |
| DOI: | 10.1007/978-3-030-26354-6_19 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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