Machine Learning Techniques to Predict Autism Spectrum Disorder

Tyagi, Bhawana; Mishra, Rahul; Bajpai, Neha

doi:10.1109/punecon.2018.8745405

Cited by 20 publications

(10 citation statements)

References 7 publications

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“…The main contribution of studies [6,47,48] is that the exploration of ASD using classification algorithms trained with a set of attributes produces outstanding prediction results based on sociodemographic features. The studies [18,[49][50][51][52] were aimed at utilizing feature selection to determine common influential attributes that are usually selected by feature selection methods and have a direct impact on the classification performance of predicting/screening tools, which shows a comparison of the performance of different prediction algorithms to diagnose ASD, which uses more than one algorithm and determines the best model based on the accuracy [53][54][55][56].…”

Section: Diagnosis Of Asd Towardmentioning

confidence: 99%

“…Furthermore, there is a requirement to develop fast medical diagnostic systems using the integration of ML methods [44]. AI algorithms have been used to save costs for human diagnosis and improve the quality of prediction [54,55,66]. ASD prediction can facilitate the time and effort of the medical staff based on the efficiency of the prediction model.…”

Section: Improving Early Diagnosis and Treatmentmentioning

confidence: 99%

“…People diagnosed with ASD have a range of symptoms, and that is why it is termed a "spectrum" disorder. 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].…”

Section: Concerns Of Asd Communication Skillsmentioning

confidence: 99%

See 2 more Smart Citations

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 Towardmentioning

confidence: 99%

Section: Improving Early Diagnosis and Treatmentmentioning

confidence: 99%

Section: Concerns Of Asd Communication Skillsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…In comparison to females, males are more affected by ASD. Research shows that genetic factors play an important role in the development of this disease [7]. Scientists have yet to discover the true causes of ASD.…”

Section: Introductionmentioning

confidence: 99%

“…Scientists have yet to discover the true causes of ASD. People with Asperger's syndrome (which is similar to autism) have average or above-average intelligence [7]. Low birth weight, having an ASD sibling, and having elderly parents increase someone's risk of developing ASD [7].…”

Section: Introductionmentioning

confidence: 99%

Development of an Interactive Dashboard for Analyzing Autism Spectrum Disorder (ASD) Data using Machine Learning

Saha¹,

Barua²,

Mishu³

et al. 2022

IJITCS

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Autism Spectrum Disorder (ASD) is a neuro developmental disorder that affects a person's ability to communicate and interact with others for rest of the life. It affects a person's comprehension and social interactions. Furthermore, people with ASD experience a wide range of symptoms, including difficulties while interacting with others, repeated behaviors, and an inability to function successfully in other areas of everyday life. Autism can be diagnosed at any age and is referred to as a "behavioral disorder" since symptoms usually appear in the life's first two years. The majority of individuals are unfamiliar with the illness and so don't know whether or not a person is disordered. Rather than aiding the sufferer, this typically leads to his or her isolation from society. The problem with ASD starts in childhood and extends into adolescence and adulthood. In this paper, we studied 25 research articles on autism spectrum disorder (ASD) prediction using machine learning techniques. The data and findings of those publications using various approaches and algorithms are analyzed. Techniques are primarily assessed using four publicly accessible non-clinically ASD datasets. We found that support vector machine (SVM) and Convolutional Neural Network (CNN) provides most accurate results compare to other techniques. Therefore, we developed an interactive dashboard using Tableau and Python to analyze Autism data.

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Towards an automatic early screening system for autism spectrum disorder in toddlers based on eye-tracking

Fernandez-Lanvin,

Gonzalez-Rodriguez,

De-Andres

et al. 2023

Multimed Tools Appl

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According to official estimations, autism spectrum disorder (ASD) affects around 1% of European newborns. The high level of dependency of ASD-affected subjects entails an extremely high social and economic cost. However, early intervention can drastically improve children’s development and thus reduce their dependency. One of the main common characteristics of subjects with ASD is difficulties with social interaction, which determines how they react to certain stimuli. This behavior can be automatically detected by analyzing their gaze. This study explores and evaluates the feasibility of automatic screening for ASD in toddlers under 24 months of age based on this specific behavior. We applied a matched pairs experimental design and a set of test videos, using a set of variables extracted from gaze analysis from toddlers using eye-tracking devices. The different videos try to capture social engagement, social information gathering gaze exchanges, and gaze following. We used the data to make a thorough comparison of machine learning algorithms (nine learning schemes), including some that were used in related prior research, and others that are popular in classification problems. The results show that several of the tested algorithms provided notable performance.

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Machine Learning Techniques to Predict Autism Spectrum Disorder

Cited by 20 publications

References 7 publications

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

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

Development of an Interactive Dashboard for Analyzing Autism Spectrum Disorder (ASD) Data using Machine Learning

Towards an automatic early screening system for autism spectrum disorder in toddlers based on eye-tracking

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