Abstract
Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance.
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KKMR conceived the idea, gathered the data, designed and coded the algorithms, tested them, and wrote the manuscript. MMS provided supervision of the project, analyzed the results, and reviewed the manuscript.
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Mujeeb Rahman, K.K., Monica Subashini, M. A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT). J Autism Dev Disord 52, 2732–2746 (2022). https://doi.org/10.1007/s10803-021-05141-2
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DOI: https://doi.org/10.1007/s10803-021-05141-2