Retraction Note to: An improved adaptive neuro fuzzy interference system for the detection of autism spectrum disorder

Pavithra, D.; Jayanthi, A. N.

doi:10.1007/s12652-022-04053-y

Cited by 1 publication

(4 citation statements)

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“…ML can predict and detect ASD, which in turn helps the medical staff in diagnosing autism and facilitates the work of the doctor [13,39,43,44]. Therefore, ML classifiers can predict the presence of autism attributes in a person of any age, which helps in the faster recovery process [45]. In [46], the Logistic Regression Model includes data and feature engineering, model training, and model testing has been presented.…”

Section: Diagnosis Of Asd Towardmentioning

confidence: 99%

“…This sort of autism spectrum syndrome is detected a great deal later than is conceivable [60]. 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].…”

Section: Improving Early Diagnosis and Treatmentmentioning

confidence: 99%

“…Since ASD is a neurological developmental disorder, there is no specific medical test, whereas its symptoms may vary from one person to another [42,54]. The reason for ASD is still under research for healthcare [18,45]. Thus, making the diagnosis of ASD an arduous task [57].…”

Section: Concerns Of Asd Communication Skillsmentioning

confidence: 99%

“…Besides, it can be extended for behavioral and sociodemographic data analysis on other datasets with implementation [44] (8) Deep learning algorithms can be utilized, and helping medical staff makes more accurate decisions on autism [45] (9) The ML classification can be affected by data imbalance on unstructured and more complex acoustic data features of autism [67] and specific gender groups [6]. In addition, studies on ASD in certain countries or at specific ages may also be conducted [65] (11) Improvement of multilayer fuzzy cognitive map MFCM-ASD through introducing new layers to evaluate ASD can represent new dimensions of symptoms to be included in the diagnostic process [40] 6.…”

Section: Recommendation and Futurementioning

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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