期刊
COMPUTERS
卷 12, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/computers12050092
关键词
autism spectrum disorder; machine learning; feature transformation; feature selection; hyper-parameter optimization
Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely affects cognitive, linguistic, object recognition, interpersonal, and communication skills. Our machine learning (ML) architecture effectively analyzes autistic children's datasets and accurately classifies and identifies ASD traits. Early diagnosis using our proposed framework could be helpful for clinicians and reduce medical costs.
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.
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