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

Translated title of the contribution: Classification of levels of gross motor function measure through machine learning techniques

Jose Alfredo Sulla Torres, Juan Carlos Copa Pineda, Raúl Sulla Torres

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Translated title of the contributionClassification of levels of gross motor function measure through machine learning techniques
Original languageSpanish
Title of host publication18th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtitle of host publication"Engineering, Integration, and Alliances for a Sustainable Development" "Hemispheric Cooperation for Competitiveness and Prosperity on a Knowledge-Based Economy", LACCEI 2020
PublisherLatin American and Caribbean Consortium of Engineering Institutions
ISBN (Electronic)9789585207141
DOIs
StatePublished - 2020
Externally publishedYes
Event18th 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
Duration: 27 Jul 202031 Jul 2020

Publication series

NameProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
ISSN (Electronic)2414-6390

Conference

Conference18th 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
CityVirtual, Online
Period27/07/2031/07/20

Bibliographical note

Funding Information:
A la Universidad Cat?lica de Santa Mar?a, Arequipa-Per? quien ha financiado el proyecto aprobado con 25789-R-2018-UCSM por el financiamiento otorgado para el desarrollo del art?culo.

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

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