Analysis of Autism Spectrum Disorder in Children with the Prepared Dataset: A Machine Learning Approach for Prediction

2020 ◽  
Author(s):  
Paruchuri Ramya ◽  
Guttikonda Geetha ◽  
Vattikuti NagaPrudhvi Raj
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine learning approach to identify resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder

2018 ◽  
Author(s):  
Bun Yamagata ◽  
Takashi Itahashi ◽  
Junya Fujino ◽  
Haruhisa Ohta ◽  
Motoaki Nakamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Connectivity Pattern ◽  
Learning Approach ◽  
Functional Connections ◽  
Communication Impairment ◽  
Machine Learning Approach

AbstractEndophenotype refers to a measurable and heritable component between genetics and diagnosis and exists in both individuals with a diagnosis and their unaffected siblings. We aimed to identify a pattern of endophenotype consisted of multiple connections. We enrolled adult male individuals with autism spectrum disorder (ASD) endophenotype (i.e., individuals with ASD and their unaffected siblings) and individuals without ASD endophenotype (i.e., pairs of typical development (TD) siblings) and utilized a machine learning approach to classify people with and without endophenotypes, based on resting-state functional connections (FCs). A sparse logistic regression successfully classified people as to the endophenotype (area under the curve=0.78, classification accuracy=75%), suggesting the existence of endophenotype pattern. A binomial test identified that nine FCs were consistently selected as inputs for the classifier. The least absolute shrinkage and selection operator with these nine FCs predicted severity of communication impairment among individuals with ASD (r=0.68, p=0.021). In addition, two of the nine FCs were statistically significantly correlated with the severity of communication impairment (r=0.81, p=0.0026 and r=-0.60, p=0.049). The current findings suggest that an ASD endophenotype pattern exists in FCs with a multivariate manner and is associated with clinical ASD phenotype.

A Machine Learning Approach to Predict and Classify the Levels of Autism Spectrum Disorder

2021 ◽  
pp. 961-967
Author(s):  
T. Anandhi ◽  
A. Srihari ◽  
G. Eswar ◽  
P. Ajitha ◽  
A. Sivasangari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Learning Approach ◽  
Machine Learning Approach

The Classification System and Biomarkers for Autism Spectrum Disorder: A Machine Learning Approach

2021 ◽  
pp. 289-299
Author(s):  
Zhongyang Dai ◽  
Haishan Zhang ◽  
Feifei Lin ◽  
Shengzhong Feng ◽  
Yanjie Wei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Classification System ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Learning Approach ◽  
Machine Learning Approach
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