Main variables predicting motor skills: analysis with classification trees

Mamani-Ramos A.A.; Damian-Nuñez E.F.; Carpio-Vargas E.E.; Mujica-Bermúdez I.; Pérez-Reátegui C.M.; Botton-Estrada L.M.; Quisocala-Ramos J.A.; Quispe-Cruz H.; Cutimbo-Quispe C.V.; Rodriguez-Mamani J.R.; Palomino-Crisóstomo R.P.; Cutipa-Salluca W.R.; Tuero-Chirinos K.F.; Villanueva-Alvaro N.S.; Lava-Gálvez J.J. · Retos (2025) · DOI: 10.47197/retos.v68.113239

Resumen

Introduction: Motor skills is a variable that marks the life of the human being. For this reason, predictive studies that explain the behavior of this variable are transcendental. Objective: The purpose of this study was to explore the main variables that predict motor skills according to classification tree analysis. Methods: A total of 291 Peruvian children aged 6 to 10 years (M=8.35; SD=1.29) participated in the study. They underwent a gross motor development test; a mathematics and reading test; a sociodemographic questionnaire; and body mass and height measurements. Results: The prediction results showed an initial model with 22 terminal nodes with 65.52% accuracy, and an optimized model with 10 terminal nodes with 68.34% accuracy. Discussion: This is the first study that applies machine learning by means of the classification tree model based on the CRISP-DM methodology to explore the main variables that predict motor skills in children aged 6 to 10 years. Conclusions: This study confirms that machine learning using classification tree modeling based on CRISP-DM methodology can predict motor skills in children aged 6 to 10 years with an accuracy of 68.34 %, with hours of physical activity practice per day being the most important variable, in addition to hours of screen device use per day and body mass of seven variables.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad Nacional de la Amazonía Peruana; Universidad Nacional del Altiplano; Universidad Nacional de San Cristóbal de Huamanga; Universidad Nacional de Educación Enrique Guzmán y Valle