Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders
Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities that are not curable but may be ameliorated by early interventions. We gathered early-detected ASD datasets relating to toddlers, children, adolescents and adults, and applied several feature transformation methods, includi...
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doaj-cd245b51c15f40b38609b9e2f3baea2b2021-03-30T00:38:55ZengIEEEIEEE Access2169-35362019-01-01716650916652710.1109/ACCESS.2019.29526098895818Machine Learning-Based Models for Early Stage Detection of Autism Spectrum DisordersTania Akter0Md. Shahriare Satu1https://orcid.org/0000-0003-1007-572XMd. Imran Khan2Mohammad Hanif Ali3Shahadat Uddin4https://orcid.org/0000-0003-0091-6919Pietro Lio5Julian M. W. Quinn6Mohammad Ali Moni7https://orcid.org/0000-0002-6874-5143Department of Computer Science and Engineering, Jahangirnagar University, Savar, BangladeshDepartment of Management Information Systems, Noakhali Science and Technology University, Noakhali, BangladeshDepartment of Computer Science and Engineering, Gono Bishwabidyalay, Dhaka, BangladeshDepartment of Computer Science and Engineering, Jahangirnagar University, Savar, BangladeshComplex Systems Research Group & Project Management Program, Faculty of Engineering, The University of Sydney, Sydney, NSW, AustraliaComputer Laboratory, University of Cambridge, Cambridge, U.K.Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, AustraliaBone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, AustraliaAutism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities that are not curable but may be ameliorated by early interventions. We gathered early-detected ASD datasets relating to toddlers, children, adolescents and adults, and applied several feature transformation methods, including log, Z-score and sine functions to these datasets. Various classification techniques were then implemented with these transformed ASD datasets and assessed for their performance. We found SVM showed the best performance for the toddler dataset, while Adaboost gave the best results for the children dataset, Glmboost for the adolescent and Adaboost for the adult datasets. The feature transformations resulting in the best classifications was sine function for toddler and Z-score for children and adolescent datasets. After these analyses, several feature selection techniques were used with these Z-score-transformed datasets to identify the significant ASD risk factors for the toddler, child, adolescent and adult subjects. The results of these analytical approaches indicate that, when appropriately optimised, machine learning methods can provide good predictions of ASD status. This suggests that it may possible to apply these models for the detection of ASD in its early stages.https://ieeexplore.ieee.org/document/8895818/ASDAQ-10 toolsclassifierFTFSTprediction model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tania Akter Md. Shahriare Satu Md. Imran Khan Mohammad Hanif Ali Shahadat Uddin Pietro Lio Julian M. W. Quinn Mohammad Ali Moni |
spellingShingle |
Tania Akter Md. Shahriare Satu Md. Imran Khan Mohammad Hanif Ali Shahadat Uddin Pietro Lio Julian M. W. Quinn Mohammad Ali Moni Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders IEEE Access ASD AQ-10 tools classifier FT FST prediction model |
author_facet |
Tania Akter Md. Shahriare Satu Md. Imran Khan Mohammad Hanif Ali Shahadat Uddin Pietro Lio Julian M. W. Quinn Mohammad Ali Moni |
author_sort |
Tania Akter |
title |
Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders |
title_short |
Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders |
title_full |
Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders |
title_fullStr |
Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders |
title_full_unstemmed |
Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders |
title_sort |
machine learning-based models for early stage detection of autism spectrum disorders |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities that are not curable but may be ameliorated by early interventions. We gathered early-detected ASD datasets relating to toddlers, children, adolescents and adults, and applied several feature transformation methods, including log, Z-score and sine functions to these datasets. Various classification techniques were then implemented with these transformed ASD datasets and assessed for their performance. We found SVM showed the best performance for the toddler dataset, while Adaboost gave the best results for the children dataset, Glmboost for the adolescent and Adaboost for the adult datasets. The feature transformations resulting in the best classifications was sine function for toddler and Z-score for children and adolescent datasets. After these analyses, several feature selection techniques were used with these Z-score-transformed datasets to identify the significant ASD risk factors for the toddler, child, adolescent and adult subjects. The results of these analytical approaches indicate that, when appropriately optimised, machine learning methods can provide good predictions of ASD status. This suggests that it may possible to apply these models for the detection of ASD in its early stages. |
topic |
ASD AQ-10 tools classifier FT FST prediction model |
url |
https://ieeexplore.ieee.org/document/8895818/ |
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