Cargando…
A comparison of machine learning algorithms for the surveillance of autism spectrum disorder
OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human clas...
Autores principales: | Lee, Scott H., Maenner, Matthew J., Heilig, Charles M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760799/ https://www.ncbi.nlm.nih.gov/pubmed/31553774 http://dx.doi.org/10.1371/journal.pone.0222907 |
Ejemplares similares
-
Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder
por: Maenner, Matthew J., et al.
Publicado: (2016) -
Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition
por: Olaguez-Gonzalez, Juan M., et al.
Publicado: (2023) -
Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning
por: Mellema, Cooper J., et al.
Publicado: (2022) -
Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms
por: Siddiqui, Uzma Abid, et al.
Publicado: (2021) -
Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms
por: Alsaade, Fawaz Waselallah, et al.
Publicado: (2022)