Clasificación de Niveles de Medida de la Función Motora Gruesa mediante Técnicas de Aprendizaje Automático

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Resumen

The aim of the article is to classify the levels of gross motor function measurement (GMFCS) in minors using machine learning techniques. The study elements were 16 patients, boys, and girls between 2 and 9 years of age from a rehabilitation and physiotherapy institution suffering from cerebral palsy in gross motor function. The clinical analysis, the application of therapy and its measurement of gross motor function were collected, then the classification of nine machine learning algorithms was applied: k-Nearest Neighbor (k-NN), Gradient Boosted tree, Decision Stump, Random Tree, Rule Induction, Improved Neural Net, Generalized Linear Model, SVM, and Linear Discriminant Analysis, which were compared based on accuracy. The results obtained showed that the Linear Discriminant Model was the one that gave the best result with a 96.88 classification accuracy. Therefore, it is concluded that the use of machine learning techniques allows obtaining good accuracy in the classification of the measured level of gross motor function in boys and girls that can be used by specialists to carry out this task.

Título traducido de la contribuciónClassification of levels of gross motor function measure through machine learning techniques
Idioma originalEspañol
Título de la publicación alojada18th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtítulo de la publicación alojada"Engineering, Integration, and Alliances for a Sustainable Development" "Hemispheric Cooperation for Competitiveness and Prosperity on a Knowledge-Based Economy", LACCEI 2020
EditorialLatin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)9789585207141
DOI
EstadoPublicada - 2020
Publicado de forma externa
Evento18th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Engineering, Integration, and Alliances for a Sustainable Development" "Hemispheric Cooperation for Competitiveness and Prosperity on a Knowledge-Based Economy", LACCEI 2020 - Virtual, Online
Duración: 27 jul. 202031 jul. 2020

Serie de la publicación

NombreProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
ISSN (versión digital)2414-6390

Conferencia

Conferencia18th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Engineering, Integration, and Alliances for a Sustainable Development" "Hemispheric Cooperation for Competitiveness and Prosperity on a Knowledge-Based Economy", LACCEI 2020
CiudadVirtual, Online
Período27/07/2031/07/20

Nota bibliográfica

Publisher Copyright:
© 2020 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Palabras clave

  • Disability
  • Gross Motor Function Classification System
  • Linear Discriminant Model
  • Machine Learning

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