TY - GEN
T1 - Clasificación de la densidad mineral ósea utilizando técnicas de aprendizaje automático en niños y adolescentes según edad y sexo
AU - Sulla-Torres, José
AU - Bedoya-Carrillo, Alan
AU - Gomez-Campos, Rossana
AU - Cossio-Bolaños, Marco
N1 - Publisher Copyright:
© 2019 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Bone health is a field that has become very important in recent years, especially in diseases related to bones, since they are becoming more common among humans. Osteoporosis currently causes an estimated 8.9 million fractures annually. Bone mineral density (BMD) and bone mineral content (BMC) are indicators that can diagnose the problem of bone health. The objective of this study is to classify BMD in children and adolescents using automatic learning techniques. A descriptive cross-sectional study was developed. We studied 660 schoolchildren from two educational centers with an age range of 6 to 18 years from the province of Arequipa (Peru). Anthropometric variables were evaluated. The BMD and CMO were determined. The Body Mass Index (BMI) was calculated, and a comparative study was made of 9 machine learning algorithms related to the subject. These include decision trees, bayesian networks, decision and regression tables. Random Forest's classification algorithm is 94.87%. This algorithm allowed to implement a software. This tool allows to calculate the bone health of schoolchildren between 6 to 18 years. The algorithm obtained can be implemented from a prediction software that allows the classification and prevention of the deterioration of the bone health of children and adolescents.
AB - Bone health is a field that has become very important in recent years, especially in diseases related to bones, since they are becoming more common among humans. Osteoporosis currently causes an estimated 8.9 million fractures annually. Bone mineral density (BMD) and bone mineral content (BMC) are indicators that can diagnose the problem of bone health. The objective of this study is to classify BMD in children and adolescents using automatic learning techniques. A descriptive cross-sectional study was developed. We studied 660 schoolchildren from two educational centers with an age range of 6 to 18 years from the province of Arequipa (Peru). Anthropometric variables were evaluated. The BMD and CMO were determined. The Body Mass Index (BMI) was calculated, and a comparative study was made of 9 machine learning algorithms related to the subject. These include decision trees, bayesian networks, decision and regression tables. Random Forest's classification algorithm is 94.87%. This algorithm allowed to implement a software. This tool allows to calculate the bone health of schoolchildren between 6 to 18 years. The algorithm obtained can be implemented from a prediction software that allows the classification and prevention of the deterioration of the bone health of children and adolescents.
KW - Bone mineral density
KW - Children and adolescents
KW - Classification
KW - Decision tree
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85073602985&partnerID=8YFLogxK
M3 - Contribución a la conferencia
AN - SCOPUS:85073602985
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - 17th LACCEI International Multi-Conference for Engineering, Education, and Technology
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 17th LACCEI International Multi-Conference for Engineering, Education, and Technology, LACCEI 2019
Y2 - 24 July 2019 through 26 July 2019
ER -