Autism Spectrum Disorder Detection in Toddlers for Early Diagnosis Using Machine Learning

Islam, Shirajul; Akter, Tahmina; Zakir, Sarah; Sabreen, Shareea; Hossain, Muhammad Iqbal

doi:10.1109/csde50874.2020.9411531

Cited by 15 publications

(10 citation statements)

References 3 publications

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“…In addition, recent research has proven that early detection of autism leads to the furry assistance provided by doctors, making it easier for the patient to recover faster. In [4], the study is aimed at estimating ASD at a sooner possible time and increasing accuracy, reducing medical costs. The final goal is to create an online tool that can provide ML-based analysis to a user to detect autism early.…”

Section: Diagnosis Of Asd Based On Questionnaires Andmentioning

confidence: 99%

“…Detecting ASD at an early step also prevents the affected condition from further deteriorating [45]. ASD must be diagnosed early to be more reliable and stable [4,61]. On the other hand, early diagnosis has a significant impact on intervention and treatment [47,50,53], decreasing and saving necessary healthcare costs [46,62].…”

Section: Improving Early Diagnosis and Treatmentmentioning

confidence: 99%

“…Unfortunately, some families and adult patients are insensible of ASD features that may be exhibited [49]. That is why these children are not able to have proper treatment at an early age, which causes more complexity in their health [4]. Furthermore, this highly complex research environment makes it even harder for practicing physicians and primary care providers to keep up with recent advances [59].…”

Section: Misdiagnosis and Lack Of Awareness For Asdmentioning

confidence: 99%

“…Autism was first characterized by Leo Kanner in 1943, who tried to understand the relationship between autism and sociodemographic factors such as social class, sex, maternal education, age, and race [1][2][3]. According to the World Health Organization (WHO), every year, out of 160 children worldwide, one child is diagnosed with autism [4]. In developed countries, about 1.5% of children were diagnosed with ASD in 2017 [5].…”

Section: Introductionmentioning

confidence: 99%

“…A diagnosis of autism can come at any age, so early detection can make a big difference in treating it later [15]. Therefore, early detection can improve a child's skills through treatment [4]. This has encouraged researchers to investigate new solutions for the early diagnosis of autism, as they help recover and improve treatment [16].…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review

Alqaysi

Albahri

Hamid

2022

International Journal of Telemedicine and Applications

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Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” ( n = 39 ). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” ( n = 1 ). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.

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Section: Diagnosis Of Asd Based On Questionnaires Andmentioning

confidence: 99%