Autism screening: an unsupervised machine learning approach

Thabtah, Fadi; Spencer, Robinson; Abdelhamid, Neda; Kamalov, Firuz; Wentzel, Carl; Ye, Yongsheng; Dayara, Thanu

doi:10.1007/s13755-022-00191-x

Cited by 10 publications

(20 citation statements)

References 44 publications

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“…ASD is a developmental brain disorder that includes difficulties that limit certain social and communication behaviors of natural growth (Thabtah et al, 2022). Although the patient's symptoms portray the beginning of the disorder, it is important to pay attention to the fact that the severity varies according to the manifestation of these symptoms and that even if they are reduced and have a change in their prognosis, from procedures performed early, there is no cure for ASD.…”

Section: Autism Spectrum Disordermentioning

confidence: 99%

“…Although ASD originated in the first years of life, its initial trajectory is not the same. In some children, for example, symptoms are noticed soon after birth, but in most cases the symptoms are only identified between 12 and 24 months (Thabtah et al, 2022). However, the diagnosis of ASD is performed at an average of 4-5 years of age.…”

Section: Autism Spectrum Disordermentioning

confidence: 99%

“…For some time, researchers (Thabtah et al, 2022) have been adhering to efficient machine learning (ML) techniques (Carvalho et al, 2019;Lopes & Lima, 2021) with the aim of improving the ASD diagnosis method by improving symptom analysis time and accuracy to provide patients with faster access to treatment. It is currently perceived that there is a large amount of accessible data capable of overcoming the effectiveness of manual analysis.…”

Section: Introductionmentioning

confidence: 99%

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Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis

Dornelas¹,

Lima²

2023

JDSIS

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Autism spectrum disorder is currently considered one of the main neurodevelopmental disorders with predominant characteristics of difficulty in social communication and cognitive skills, and limited and repetitive patterns. This disorder has no cure and has different levels of severity that vary according to the appearance of symptoms in each patient. Generally, the waiting time for the diagnosis of autism spectrum disorder is slow, having as one of the reasons for this situation the lack of development of simple screening procedures to be implemented and which have efficient results. The objective of this work is to analyze a public database in order to find patterns of the autism spectrum. That is, to isolate the attributes that together with behavioral characteristics can bring greater reliability to the precursor model. The preliminary results showed that the probabilistic neural network algorithm performed well in this classification. In addition, the application of correlation filters demonstrated greater efficiency in accuracy. By applying 8 data mining algorithms and aggregating the demographic, individual and behavioral attributes, and excluding some attributes, we obtained an accuracy of 100% through the Support Vector Machine. Finally, the results with machine learning have shown that the patient’s ethnicity, continent and the presence of jaundice, tend to reveal more likely that the patient will be diagnosed with autism spectrum disorder.

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Section: Autism Spectrum Disordermentioning

confidence: 99%

Section: Autism Spectrum Disordermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis

Dornelas¹,

Lima²

2023

JDSIS

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“…ASD causes social difficulties, sensory issues, repetitive behaviour, and intellectual incapacity. ASD patients also have psychiatric or neurological disorders such as hyperactivity, attention deficit, anxiety, sadness, and epilepsy [2].…”

Section: Introductionmentioning

confidence: 99%

“…Autism symptoms are more apparent and easier to recognize in youngsters between 2 and 3 years old. ASD affects one in 68 children in the United States, according to [2]. Existing statistics indicate that approximately 1.5% of the global population is autistic, and it is assumed that a large proportion of autistic people remain unidentified.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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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Resampling Strategies for Mitigating Class Imbalance of ASD Dataset on the Performance of Machine Learning Classifiers

Gupta,

Dutta

2023

Lecture Notes in Networks and Systems

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Autism screening: an unsupervised machine learning approach

Cited by 10 publications

References 44 publications

Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis

Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis

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

Resampling Strategies for Mitigating Class Imbalance of ASD Dataset on the Performance of Machine Learning Classifiers

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