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

Yolvi J. Ocaña-Fernández, Walter Gómez-Gonzales, Luis Alex Valenzuela Fernández, Segundo Pio Vásquez Ramos, Huguette Fortunata Dueñas Zúñiga, Jackeline Roxana Huaman Fernandez, Marco Antonio Amapanqui Broncano

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)59-65
Number of pages7
JournalJournal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
Volume14
Issue number2
DOIs
StatePublished - Jun 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023, Innovative Information Science and Technology Research Group. All rights reserved.

Keywords

  • ASD
  • Diagnosis
  • Learning Disabilities
  • Logistic Regression (LR) and SVM
  • Multinomial NB

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