Abstract
Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.
Highlights
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that causes persistent deficits in children’s social communication skills and behavior
Evaluation Metrics Utilized for Classification of ASD by Machine Learning
In the study of ASD, diagnostic performance is vital and can be enhanced to classify the exact type of ASD accurately and cost-effectively. This can be accomplished in various ways, such as increasing predictive accuracy, maintaining sensitivity, specificity, and validity, and reducing diagnostic time
Summary
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that causes persistent deficits in children’s social communication skills and behavior. Manual of Mental Disorders 5 (DSM-5) [1], social communication skills are described as; (i) deficits in social-emotional reciprocity, (ii) deficits in nonverbal communicative behavior, and (iii) deficits in developing, maintaining, and understanding relationships. Deficits in behavior are defined as restricted and repetitive patterns of behavior and fixated interest among children with autism, which is referred to as (i) stereotyped or repetitive motor movements, (ii) insistence on sameness, difficulties with changes in routine or rigid patterns of verbal or nonverbal behavior,. (iii) fixated interest, and (iv) unusual response to sensory aspects. Children with sensory issues may have hyper-sensitivities or hypo-sensitivities to a wide range of senses such as sights, sounds, smells, tastes, touch, balance, and body awareness. Other issues that often relate to sensory issues are flailing of the hands or arms, walking on tiptoes, swinging back and forth, and self-harm
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
