Book ChapterDOI
A Meta-Heuristic Model Based Computational Intelligence in Exploration and Classification of Autism in Children
S. P. Abirami,G. Kousalya,P. Balakrishnan +2 more
- pp 61-77
Reads0
Chats0
TLDR
The novelty of the paper lies in the fact of extracting important features for modeling so as to make a prior analysis by any parents at home before approaching clinicians which supports the early intervention of autism.Abstract:
Autism spectrum disorder (ASD) is one of the most notable neurodevelopmental disorders that gained major notification among parents, clinicians and even in researchers in the current era. The early identification of autism is a much needed support for parents and clinicians. The proposed methodology aims in building a computational model for such easy and early diagnosis by analyzing and finding the correlations between features-to-class and feature-to-feature so as to maximize the former and minimize the latter. The correlation between features is analyzed using (i) chi square computation technique in filter method and (ii) information gain. On analyzing the correlations, the resultant attributes of every technique are trained separately under the standard linear SVM classifier and then tested for the models performance and accuracy. There are two major contributions of the proposed work; Method 1: to build a model that takes optimized features extracted from the chi square and information gain analysis from questionnaires on the application of genetic algorithm (GA). The optimized features are then trained and tested to classify autism in support of SVM linear classifier. Method 2: to build a model based on the application of back-propagation feed forward neural network to classify the presence of autism. The paper ensures better and faster convergence of the positive class label of autism with maximized accuracy, specificity, performance and minimized error. The novelty of the paper lies in the fact of extracting important features for modeling so as to make a prior analysis by any parents at home before approaching clinicians which supports the early intervention of autism.read more
References
More filters
Journal ArticleDOI
Machine learning in autistic spectrum disorder behavioral research: A review and ways forward
TL;DR: This article critically analyses recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data.
Journal ArticleDOI
Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises
Daniel Bone,Matthew S. Goodwin,Matthew P. Black,Chi-Chun Lee,Kartik Audhkhasi,Shrikanth S. Narayanan +5 more
TL;DR: Proposed best-practices when using machine learning in autism research are highlighted, and some especially promising areas for collaborative work at the intersection of computational and behavioral science are highlighted.
Journal ArticleDOI
Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism
TL;DR: An initial attempt to retrospectively analyze large data repositories to derive an accurate, but significantly abbreviated approach that may be used for rapid detection and clinical prioritization of individuals likely to have an autism spectrum disorder.
Journal ArticleDOI
Confirmatory Factor Analysis of the Child Behavior Checklist 1.5–5 in a Sample of Children with Autism Spectrum Disorders
TL;DR: It is suggested that practitioners can use the CBCL to assess for EBD in young children with ASD in conjunction with other clinical data to increase the likelihood of accurate identification and EBD-specific intervention.
Proceedings ArticleDOI
Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment
TL;DR: Light is shed on recent studies that employ machine learning in ASD classification in order to discuss their pros and cons and a noticeable problem associated with current ASD screening tools; the reliability of these tools using the DSM-IV rather than the DSM -5 manual is highlighted.