An Application of Neural Networks to Predicting Mastery of Learning Outcomes in the Treatment of Autism Spectrum Disorder

Autor:	Dennis R. Dixon, Erik Linstead, Alva Powell, Marlena N. Novack, Doreen Granpeesheh, Rene German
Rok vydání:	2015
Předmět:	Artificial neural network Computer science business.industry Behavioral therapy medicine.disease Machine learning computer.software_genre Task (project management) Autism spectrum disorder Sample size determination medicine Autism Artificial intelligence business computer
Zdroj:	ICMLA
DOI:	10.1109/icmla.2015.214
Popis:	We apply artificial neural networks to the task of predicting the mastery of learning outcomes in response to behavioral therapy for children diagnosed with autism spectrum disorder. We report results for a sample size of 726 children, the largest sample size reported for a study of this nature to date. Our results show that neural networks substantially outperform the linear regression models reported in previous studies, and demonstrate the benefits of leveraging more sophisticated machine learning techniques in the autism research domain.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fbfb7f0b867d89c8039b84d3b0ab776b https://doi.org/10.1109/icmla.2015.214 Zobrazit plný text záznamu