TY - GEN
T1 - Clasificación de Niveles de Medida de la Función Motora Gruesa mediante Técnicas de Aprendizaje Automático
AU - Sulla-Torres, José
AU - Pineda, Juan Carlos Copa
AU - Torres, Raúl Sulla
N1 - Publisher Copyright:
© 2020 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
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.
KW - Disability
KW - Gross Motor Function Classification System
KW - Linear Discriminant Model
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85096770279&partnerID=8YFLogxK
U2 - 10.18687/LACCEI2020.1.1.216
DO - 10.18687/LACCEI2020.1.1.216
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
T2 - 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
Y2 - 27 July 2020 through 31 July 2020
ER -