Classifying Autism Spectrum Disorder using Machine Learning Models

2021 ◽  
Author(s):  
Tingyan Deng
Keyword(s):  
Logistic Regression ◽  
Autistic Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Autistic Spectrum ◽  
Time Treatment ◽  
Proposed Model ◽  
Related Feature ◽  
And Behavior ◽  
Logistic Regression Classifier

Autistic Spectrum Disorder (ASD) is a developmental disability, which can affect communication and behavior, causing significant social, communication, and behavior challenge. From a rare childhood disorder, ASD has evolved into a disorder that is found, according to the National Institute of Health, in 1% to 2% of the population in high income countries. A potential early and accurate diagnosis can not only help doctors to find the disease early, leading to a more on time treatment to the patient, but also can save significant healthcare costs for the patients. With the rapid growth of ASD cases, many open-source ASD related datasets were created for scientists and doctors to investigate this disease. Autistic Spectrum Disorder Screening Data for Adult is a well-known dataset, which contains 20 features to be utilized for further analysis on the potential cause and prediction of ASD. In this paper, we developed an Autism classification algorithm based on logistic regression model. Our model starts with featuring engineering to extract deep information from the dataset and then applied a modified logistic regression classifier to the data. The model can predict the ASD in an average F1 score of 0.97, which displays the superiority and feasibility of the proposed model. Besides, the data visualization technique was used to displays several feature distributions images for people to better understand the data and related feature engineering.

scholarly journals Improvement in excoriation (skin-picking) with use of risperidone in a patient with developmental disability

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Cody Roi ◽  
Alessandra Bazzano
Keyword(s):  
Behavioral Interventions ◽  
Autistic Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Skin Picking ◽  
Autistic Spectrum ◽  
Valuable Treatment ◽  
Skin Picking Disorder ◽  
Core Symptoms ◽  
Psychosocial Complications

Patients with Autism Spectrum Disorder present with a heterogeneous mix of features beyond the core symptoms of the disorder. These features can be emotional, cognitive or behavioral. Behavioral symptoms often include self-injury, and this may take the form of repetitive skin-picking. The prevalence of skin-picking disorder in Autism is unknown. Skin-picking may lead to significant medical and psychosocial complications. Recent data suggest that behavioral interventions may be more effective than medications at reducing skin-picking in neurotypical patients. In this case, an 11-year-old male with intellectual disability and autistic spectrum disorder, with self-injurious skin-picking, was treated with risperidone with complete resolution of skin-picking symptoms. risperidone has been approved for irritability and aggression in Autistic spectrum disorder, and may be a valuable treatment option for skinpicking in pediatric patients with developmental disabilities.

scholarly journals Little is known about cannabidiol for improving severe behavioral symptoms of autistic spectrum disorder

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Keerteshwrya Mishra
Keyword(s):  
Autism Spectrum Disorder ◽  
Autistic Spectrum Disorder ◽  
Children With Autism ◽  
Clinical Decision ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Behavioral Symptoms ◽  
Autistic Spectrum

A clinical decision report appraising Barchel D, Stolar O, De-Haan T, et al. Oral cannabidiol use in children with autism spectrum disorder to treat related symptoms and co-morbidities. Frontiers in Pharmacology. 2019;9. https://doi.org/10.3389/fphar.2018.01521

Theory of Mind, Religiosity, and Autistic Spectrum Disorder: a Review of Empirical Evidence Bearing on Three Hypotheses

2019 ◽  
Vol 19 (5) ◽  
pp. 411-431
Author(s):  
Robert N. McCauley ◽  
George Graham ◽  
A. C. Reid
Keyword(s):  
Theory Of Mind ◽  
Empirical Evidence ◽  
Language Processing ◽  
Autistic Spectrum Disorder ◽  
Empirical Studies ◽  
Spectrum Disorder ◽  
Cognitive Measures ◽  
Autistic Spectrum ◽  
Intelligence Theory ◽  
And Behavior

AbstractThe cognitive science of religions’ By-Product Theory contends that much religious thought and behavior can be explained in terms of the cultural activation of maturationally natural cognitive systems. Those systems address fundamental problems of human survival, encompassing such capacities as hazard precautions, agency detection, language processing, and theory of mind. Across cultures they typically arise effortlessly and unconsciously during early childhood. They are not taught and appear independent of general intelligence. Theory of mind (mentalizing) undergirds an instantaneous and automatic intuitive understanding of minds, mental representations, and their implications for agents’ actions. By-Product theorists hypothesize about a social cognition content bias, holding that mentalizing capacities inform participants’ implicit understanding of religious representations of agents with counter-intuitive properties. That hypothesis, in combination with Baron-Cohen’s account of Autistic Spectrum Disorder (ASD) in terms of diminished theory of mind capacities (what he calls “mind-blindness”), suggests an impaired religious understanding hypothesis. It proposes that people with ASD have substantial limitations in intuitive understanding of and creative inferences from such representations. Norenzayan argues for a mind-blind atheism hypothesis, which asserts that the truth of these first two hypotheses suggests that people with ASD have an increased probability, compared to the general population, of being atheists. Numerous empirical studies have explored these three hypotheses’ merits. After carefully pondering distinctions between intuitive versus reflective mentalizing and between explicit versus implicit measures and affective versus cognitive measures of mentalizing, the available empirical evidence provides substantial support for the first two hypotheses and non-trivial support for the third.

Black South African caregivers’ understanding and responses to their children with autism spectrum disorder symptoms: a qualitative study

2017 ◽  
Vol 48 (1) ◽  
pp. 99-111 ◽  
Author(s):  
Zinhle Cynthia Mthombeni ◽  
Augustine Nwoye
Keyword(s):  
South African ◽  
Mental Health Treatment ◽  
Autistic Spectrum Disorder ◽  
Sampling Technique ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Policy And Practice ◽  
Autistic Spectrum ◽  
Black South African ◽  
Study Participants

The purpose of this study was to investigate Black South African caregivers’ understanding and approaches to securing a cure for their children with autistic spectrum disorder symptoms. Qualitative data were collected focusing on exploring caregivers’ narratives. A purposive sampling technique was used to draw study participants who were of Black South African background and had a child with a diagnosis of an autistic spectrum disorder. Thematic analysis was used to analyse the data collected and to give analytical meaning to the narratives. The results yielded four dominant themes regarding the level of caregivers’ understanding of autistic spectrum disorder and the options they followed in searching for the cure of their children’s illness. The study drew attention to the feelings of frustration experienced by caregivers in their discovery that the use of both indigenous and Western approaches to autistic spectrum disorder symptoms yielded little benefits. Given these findings, a number of recommendations were made to improve policy and practice in the mental health treatment of children with autistic spectrum disorder symptoms among Black South African clients.

scholarly journals Communicative skills of children with autistic spectrum disorder: clinical and family perception

Revista CEFAC ◽  
2021 ◽  
Vol 23 (4) ◽  
Author(s):  
Fernanda Aparecida Ferreira de Freitas ◽  
Ana Cristina de Albuquerque Montenegro ◽  
Fernanda Dreux Miranda Fernandes ◽  
Isabelle Cahino Delgado ◽  
Larissa Nadjara Alves Almeida ◽  
...  
Keyword(s):  
Autistic Spectrum Disorder ◽  
Speech Therapy ◽  
Autism Spectrum ◽  
Point Of View ◽  
Spectrum Disorder ◽  
Linguistic Knowledge ◽  
Chi Square ◽  
Communicative Skills ◽  
Autistic Spectrum ◽  
Chi Square Test

ABSTRACT Objective: to describe the communication skills of children with autistic spectrum disorder (ASD), considering the clinical and family perspective. Methods: from the point of view of parents and therapists, the language of ten children with ASD was analyzed. All children underwent speech therapy at the outpatient clinic of a Speech Therapy School. Two protocols were used for data collection. Autism Treatment Evaluation Checklist (ATEC), which was applied to the children's parents, and Protocol for Assessment of Pragmatic Skills of Children with Autism Spectrum Disorders - called Pragmatic Protocol (PP), which was answered by therapists. The data were examined through a descriptive statistical analysis, considering absolute and relative frequency, and inferential statistics, through the Chi-square test, with a 5% of significance for all analyses. Results: an expressive presence of communicative deficits, in the answers presented by the therapists, was seen. In the protocol answered by the parents, it was also possible to observe the same trend, since the children failed to score in several items of Subscale I. Conclusion: parents and therapists evidenced changes in the communicative skills of the children surveyed, and emphasized that therapists, who have technical linguistic knowledge, like parents, can also be good informants about their children's communicative development process.

Saccadic eye movements in adults with high-functioning autism spectrum disorder

Autism ◽  
2016 ◽  
Vol 22 (2) ◽  
pp. 195-204 ◽  
Author(s):  
Tiziana Zalla ◽  
Magali Seassau ◽  
Fabienne Cazalis ◽  
Doriane Gras ◽  
Marion Leboyer
Keyword(s):  
Autism Spectrum Disorder ◽  
Eye Movements ◽  
Autistic Spectrum Disorder ◽  
High Functioning Autism ◽  
Saccadic Eye Movements ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Control Group ◽  
Adults With Autism ◽  
Autistic Spectrum

In this study, we examined the accuracy and dynamics of visually guided saccades in 20 adults with autism spectrum disorder, as compared to 20 typically developed adults using the Step/Overlap/Gap paradigms. Performances in participants with autistic spectrum disorder were characterized by preserved Gap/Overlap effect, but reduced gain and peak velocity, as well as a greater trial-to-trial variability in task performance, as compared to the control group. While visual orienting and attentional engagement were relatively preserved in individuals with autistic spectrum disorder, overall these findings provide evidence that abnormal oculomotor behavior in autistic spectrum disorder reflects an altered sensorimotor control due to cerebellar abnormalities, rather than a deficit in the volitional control of eye movements. This study contributes to a growing body of evidence implicating this structure in the physiopathology of autism.

Deep Classifier Model for Autism Spectrum Disorder Prediction

2020 ◽  
Author(s):  
Raouia Mokni ◽  
Mariem Haoues
Keyword(s):  
Autism Spectrum Disorder ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Research Area ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Human Communication ◽  
Machine Learning Classification ◽  
Proposed Model ◽  
And Behavior

Autism Spectrum Disorder (ASD) is a neurological and developmental disorder that affects human communication and behavior. ASD is associated with significant healthcare costs for diagnosis as well as for treatment. Disease diagnosis using deep learning model has become a wide research area. This paper proposes a deep classifier model for ASD prediction. The evaluation of the proposed model is performed over three datasets involving child, adolescent, and adult provided by ASDTest database. The obtained results showed that deep classifier model provides better results than other common machine learning classification techniques, with an accuracy of 99.50%, 99.23% and 99.42% for respectively adult, adolescent, and child datasets. Practical experiments conducted over these datasets report encouraging performances which are competitive to other existing ASD prediction models.

scholarly journals Differential Evolution Tuned Support Vector Machine for Autistic Spectrum Disorder Diagnosis

2019 ◽  
Vol 8 (2) ◽  
pp. 3861-3870
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Autistic Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Classification Model ◽  
Support Vector ◽  
Data Set ◽  
Autistic Spectrum

Autistic Spectrum Disorder (ASD) is a brain developmental disorder which weakens the ability to communicate and interact with others. A child with autism spectrum disorder may have different, repetitive patterns of behaviour, interests or activities, including some specific signs. To diagnose the behaviour of ASD and identify the level of disease on the human is still a challenging task for the doctors. Only by the trained and experienced physician can identify the ASD immediately. The data set for autism problem consist of number of causes and the results based on the symptoms for ASD. So, Data mining algorithm is in need to organize and pattern the ASD details. The machine algorithms are available to classify the data in data mining works. In this proposed work, a machine learning algorithm called Support Vector Machine is used to classify the ASD children accurately. SVM is one of the classification algorithms which finding the hyper plane that maximizes the margin between the two classes. Though SVM give better identification of disease, some children have their unique nature which hides their problem of ASD easily. So, to diagnose the problem accurately, the user defined SVM parameters are tuned by optimization algorithm called Differential Evolutionary Algorithm. DE is an optimization algorithm used to find the optimal solution of SVM parameters. Further, to improve the performance of the proposed method, the dimension reduction technique is followed to reduce the SVM and ANN network dimension. The Sequential Feature Selection (SFS) method is applied in this paper, which select the most influenced variables for the output. The reduced network is further classified by ANN and SVM model. The Data set for the ANN and SVM network has been taken from the real records of the multi-specialty hospitals. The SVM and DE optimized SVM results are compared with another classification model called Artificial Neural Networks. The test results show the betterment of DE optimized SVM which give the classification of ASD child very accurately compare with ANN and DE optimized ANN.

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