A Novel Approach to Predict the Early Childhood Special Education Learning Skills of Autistic Children Using Ensemble Machine Learning

Ocaña-Fernández Y.J.; Gómez-Gonzales W.; Valenzuela Fernández L.A.; Vásquez Ramos S.P.; Dueñas Zúñiga H.F.; Huaman Fernandez J.R.; Amapanqui Broncano M.A. · Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (2023) · DOI: 10.58346/jowua.2023.i2.005

Resumen

Children with autism spectrum disorder will eventually receive more extensive educational experiences, diverse understanding styles, any distinctive instructional techniques to help all infants achieve. Data mining categorization algorithms in the Weka tool are used to anticipate and forecast infants' performance with Autism Spectrum Disorder (ASD). As a decision-making tool for improving the performance of autistic youngsters, data mining is widely acknowledged. Support Vector Machines (SVMs), Logistic Regression (LR), and Naive Bayes (NB) are some of the techniques that can be used for categorization. The categorization model's outcomes include information on the model's accuracy, error rate, confusion matrices, classifier effectiveness, and execution time.

Instituciones: Universidad Nacional del Altiplano; Universidad Privada San Juan Bautista; Universidad César Vallejo; Universidad Nacional de Educación Enrique Guzmán y Valle