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Article

An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder

by
Md. Jamal Uddin
1,
Md. Martuza Ahamad
1,
Prodip Kumar Sarker
2,
Sakifa Aktar
1,
Naif Alotaibi
3,
Salem A. Alyami
3,
Muhammad Ashad Kabir
4 and
Mohammad Ali Moni
5,*
1
Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
2
Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh
3
Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
4
School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia
5
Artificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
*
Author to whom correspondence should be addressed.
Computers 2023, 12(5), 92; https://doi.org/10.3390/computers12050092
Submission received: 5 March 2023 / Revised: 23 April 2023 / Accepted: 23 April 2023 / Published: 30 April 2023
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain)
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Abstract

Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely impairs cognitive, linguistic, object recognition, interpersonal, and communication skills. Its main cause is genetic, and early treatment and identification can reduce the patient’s expensive medical costs and lengthy examinations. We developed a machine learning (ML) architecture that is capable of effectively analysing autistic children’s datasets and accurately classifying and identifying ASD traits. We considered the ASD screening dataset of toddlers in this study. We utilised the SMOTE method to balance the dataset, followed by feature transformation and selection methods. Then, we utilised several classification techniques in conjunction with a hyperparameter optimisation approach. The AdaBoost method yielded the best results among the classifiers. We employed ML and statistical approaches to identify the most crucial characteristics for the rapid recognition of ASD patients. We believe our proposed framework could be useful for early diagnosis and helpful for clinicians.
Keywords: autism spectrum disorder; machine learning; feature transformation; feature selection; hyper-parameter optimization autism spectrum disorder; machine learning; feature transformation; feature selection; hyper-parameter optimization

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MDPI and ACS Style

Uddin, M.J.; Ahamad, M.M.; Sarker, P.K.; Aktar, S.; Alotaibi, N.; Alyami, S.A.; Kabir, M.A.; Moni, M.A. An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder. Computers 2023, 12, 92. https://doi.org/10.3390/computers12050092

AMA Style

Uddin MJ, Ahamad MM, Sarker PK, Aktar S, Alotaibi N, Alyami SA, Kabir MA, Moni MA. An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder. Computers. 2023; 12(5):92. https://doi.org/10.3390/computers12050092

Chicago/Turabian Style

Uddin, Md. Jamal, Md. Martuza Ahamad, Prodip Kumar Sarker, Sakifa Aktar, Naif Alotaibi, Salem A. Alyami, Muhammad Ashad Kabir, and Mohammad Ali Moni. 2023. "An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder" Computers 12, no. 5: 92. https://doi.org/10.3390/computers12050092

APA Style

Uddin, M. J., Ahamad, M. M., Sarker, P. K., Aktar, S., Alotaibi, N., Alyami, S. A., Kabir, M. A., & Moni, M. A. (2023). An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder. Computers, 12(5), 92. https://doi.org/10.3390/computers12050092

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