scholarly journals Employing Machine Learning-Based Predictive Analytical Approaches to Classify Autism Spectrum Disorder Types

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Muhammad Kashif Hanif ◽  
Naba Ashraf ◽  
Muhammad Umer Sarwar ◽  
Deleli Mesay Adinew ◽  
Reehan Yaqoob
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Neural Network ◽  
Autism Spectrum ◽  
Optimization Techniques ◽  
Spectrum Disorder ◽  
Early Age ◽  
Analytical Approaches

Autism spectrum disorder is an inherited long-living and neurological disorder that starts in the early age of childhood with complicated causes. Autism spectrum disorder can lead to mental disorders such as anxiety, miscommunication, and limited repetitive interest. If the autism spectrum disorder is detected in the early childhood, it will be very beneficial for children to enhance their mental health level. In this study, different machine and deep learning algorithms were applied to classify the severity of autism spectrum disorder. Moreover, different optimization techniques were employed to enhance the performance. The deep neural network performed better when compared with other approaches.

scholarly journals ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Fahad Almuqhim ◽  
Fahad Saeed
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Sparse Autoencoder ◽  
Deep Learning Model

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understanding of the neurological mechanisms underlying ASD necessitates the identification of new biomarkers that can aid in study of brain development, and functioning, and can lead to accurate and early detection of ASD. In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features that can be used for classification. These features are then fed into a deep neural network (DNN) which results in superior classification of fMRI brain scans more prone to ASD. Our proposed model is trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error. We evaluated our proposed deep-learning model using publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset collected from 17 different research centers, and include more than 1,035 subjects. Our extensive experimentation demonstrate that ASD-SAENet exhibits comparable accuracy (70.8%), and superior specificity (79.1%) for the whole dataset as compared to other methods. Further, our experiments demonstrate superior results as compared to other state-of-the-art methods on 12 out of the 17 imaging centers exhibiting superior generalizability across different data acquisition sites and protocols. The implemented code is available on GitHub portal of our lab at: https://github.com/pcdslab/ASD-SAENet.

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning (Preprint)

2021 ◽  
Author(s):  
Federica Cilia ◽  
Romuald Carette ◽  
Mahmoud Elbattah ◽  
Gilles Dequen ◽  
Jean-Luc Guérin ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Eye Tracking ◽  
Convolutional Neural Network ◽  
Visual Representation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Computer Assisted ◽  
Maximal Information Coefficient

BACKGROUND The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. OBJECTIVE This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening METHODS The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. RESULTS The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. CONCLUSIONS Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

Characterizing therapist delivery of evidence-based intervention strategies in publicly funded mental health services for children with autism spectrum disorder: Differentiating practice patterns in usual care and AIM HI delivery

Autism ◽  
2021 ◽  
pp. 136236132110016
Author(s):  
Eliana Hurwich-Reiss ◽  
Colby Chlebowski ◽  
Teresa Lind ◽  
Kassandra Martinez ◽  
Karin M Best ◽  
...  
Keyword(s):  
Mental Health ◽  
Autism Spectrum Disorder ◽  
Usual Care ◽  
Teaching Strategies ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Intervention Strategies ◽  
Spectrum Disorder ◽  
Evidence Based ◽  
Active Teaching

This study identified patterns of therapist delivery of evidence-based intervention strategies with children with autism spectrum disorder within publicly funded mental health services and compared patterns for therapists delivering usual care to those trained in AIM HI (“An Individualized Mental Health Intervention for ASD”). Data were drawn from a randomized community effectiveness trial and included a subsample of 159 therapists (86% female) providing outpatient or school-based psychotherapy. Therapist strategies were measured via observational coding of psychotherapy session recordings. Exploratory factor analysis used to examine patterns of strategy delivery showed that among therapists in the usual care condition, strategies loaded onto the single factor, General Strategies, whereas for therapists in the AIM HI training condition, strategies grouped onto two factors, Autism Engagement Strategies and Active Teaching Strategies. Among usual care therapists, General Strategies were associated with an increase in child behavior problems, whereas for AIM HI therapists, Active Teaching Strategies were associated with reductions in child behavior problems over 18 months. Results support the effectiveness of training therapists in evidence-based interventions to increase the specificity of strategies delivered to children with autism spectrum disorder served in publicly funded mental health settings. Findings also support the use of active teaching strategies in reducing challenging behaviors. Lay abstract This study was conducted to identify patterns of therapist delivery of evidence-based intervention strategies with children with autism spectrum disorder receiving publicly funded mental health services and compare strategy use for therapists delivering usual care to those trained to deliver AIM HI (“An Individualized Mental Health Intervention for ASD”), an intervention designed to reduce challenging behaviors in children with autism spectrum disorder. For therapists trained in AIM HI, intervention strategies grouped onto two factors, Autism Engagement Strategies and Active Teaching Strategies, while strategies used by usual care therapists grouped onto a broader single factor, General Strategies. Among usual care therapists, General Strategies were related to an increase in child behavior problems, whereas for AIM HI therapists, Active Teaching Strategies were related with reductions in child behavior problems over 18 months. Findings support the use of active teaching strategies in reducing challenging behaviors in children with autism spectrum disorder and provide support for the effectiveness of training therapists in evidence-based interventions to promote the delivery of targeted, specific intervention strategies to children with autism spectrum disorder in mental health services.

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