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

doi:10.58346/JOWUA.2023.I2.005

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 journal › Article › peer-review

19 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 language	English
Pages (from-to)	59-65
Number of pages	7
Journal	Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
Volume	14
Issue number	2
DOIs	https://doi.org/10.58346/JOWUA.2023.I2.005
State	Published - Jun 2023
Externally published	Yes

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

Access to Document

10.58346/JOWUA.2023.I2.005

Cite this

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. (2023). A Novel Approach to Predict the Early Childhood Special Education Learning Skills of Autistic Children Using Ensemble Machine Learning. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 14(2), 59-65. https://doi.org/10.58346/JOWUA.2023.I2.005

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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.",

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Ocaña-Fernández, YJ, Gómez-Gonzales, W, Valenzuela Fernández, LA, Vásquez Ramos, SP, Dueñas Zúñiga, HF, Huaman Fernandez, JR & Amapanqui Broncano, MA 2023, 'A Novel Approach to Predict the Early Childhood Special Education Learning Skills of Autistic Children Using Ensemble Machine Learning', Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 14, no. 2, pp. 59-65. https://doi.org/10.58346/JOWUA.2023.I2.005

A Novel Approach to Predict the Early Childhood Special Education Learning Skills of Autistic Children Using Ensemble Machine Learning. / Ocaña-Fernández, Yolvi J.; Gómez-Gonzales, Walter; Valenzuela Fernández, Luis Alex et al.
In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, Vol. 14, No. 2, 06.2023, p. 59-65.

Research output: Contribution to journal › Article › peer-review

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AU - Gómez-Gonzales, Walter

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AU - Vásquez Ramos, Segundo Pio

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Ocaña-Fernández YJ, Gómez-Gonzales W, Valenzuela Fernández LA, Vásquez Ramos SP, Dueñas Zúñiga HF, Huaman Fernandez JR et al. A Novel Approach to Predict the Early Childhood Special Education Learning Skills of Autistic Children Using Ensemble Machine Learning. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2023 Jun;14(2):59-65. doi: 10.58346/JOWUA.2023.I2.005

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

Abstract

Bibliographical note

Keywords

Access to Document

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Cite this