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

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

doi:10.18687/LACCEI2020.1.1.216

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 proceeding › Conference contribution › peer-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 contribution	Classification of levels of gross motor function measure through machine learning techniques
Original language	Spanish
Title of host publication	18th 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
Publisher	Latin American and Caribbean Consortium of Engineering Institutions
ISBN (Electronic)	9789585207141
DOIs	https://doi.org/10.18687/LACCEI2020.1.1.216
State	Published - 2020
Externally published	Yes
Event	18th 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 2020 → 31 Jul 2020

Publication series

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

Conference

Conference	18th 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
City	Virtual, Online
Period	27/07/20 → 31/07/20

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.

Access to Document

10.18687/LACCEI2020.1.1.216

Cite this

Sulla Torres, J. A., Pineda, J. C. C., & Torres, R. S. (2020). Clasificación de Niveles de Medida de la Función Motora Gruesa mediante Técnicas de Aprendizaje Automático. In 18th 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 (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology). Latin American and Caribbean Consortium of Engineering Institutions. https://doi.org/10.18687/LACCEI2020.1.1.216

Sulla Torres, Jose Alfredo ; Pineda, Juan Carlos Copa ; Torres, Raúl Sulla. / Clasificación de Niveles de Medida de la Función Motora Gruesa mediante Técnicas de Aprendizaje Automático. 18th 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. Latin American and Caribbean Consortium of Engineering Institutions, 2020. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

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title = "Clasificaci{\'o}n de Niveles de Medida de la Funci{\'o}n Motora Gruesa mediante T{\'e}cnicas de Aprendizaje Autom{\'a}tico",

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.",

keywords = "Disability, Gross Motor Function Classification System, Linear Discriminant Model, Machine Learning",

author = "{Sulla Torres}, {Jose Alfredo} and Pineda, {Juan Carlos Copa} and Torres, {Ra{\'u}l Sulla}",

year = "2020",

doi = "10.18687/LACCEI2020.1.1.216",

language = "Espa{\~n}ol",

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publisher = "Latin American and Caribbean Consortium of Engineering Institutions",

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Sulla Torres, JA, Pineda, JCC & Torres, RS 2020, Clasificación de Niveles de Medida de la Función Motora Gruesa mediante Técnicas de Aprendizaje Automático. in 18th 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. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, Latin American and Caribbean Consortium of Engineering Institutions, 18th 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, 27/07/20. https://doi.org/10.18687/LACCEI2020.1.1.216

Clasificación de Niveles de Medida de la Función Motora Gruesa mediante Técnicas de Aprendizaje Automático. / Sulla Torres, Jose Alfredo; Pineda, Juan Carlos Copa; Torres, Raúl Sulla.

18th 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. Latin American and Caribbean Consortium of Engineering Institutions, 2020. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Clasificación de Niveles de Medida de la Función Motora Gruesa mediante Técnicas de Aprendizaje Automático

AU - Sulla Torres, Jose Alfredo

AU - Pineda, Juan Carlos Copa

AU - Torres, Raúl Sulla

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

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KW - Gross Motor Function Classification System

KW - Linear Discriminant Model

KW - Machine Learning

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M3 - Contribución a la conferencia

AN - SCOPUS:85096770279

T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology

BT - 18th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology

PB - Latin American and Caribbean Consortium of Engineering Institutions

Y2 - 27 July 2020 through 31 July 2020

ER -

Sulla Torres JA, Pineda JCC, Torres RS. Clasificación de Niveles de Medida de la Función Motora Gruesa mediante Técnicas de Aprendizaje Automático. In 18th 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. Latin American and Caribbean Consortium of Engineering Institutions. 2020. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology). doi: 10.18687/LACCEI2020.1.1.216

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

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