Journal articles: 'Autism spectrum disorders – Classification'

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Baker, Emma K., Amanda L. Richdale, and Agnes Hazi. "Employment status is related to sleep problems in adults with autism spectrum disorder and no comorbid intellectual impairment." Autism 23, no. 2 (February 18, 2018): 531–36. http://dx.doi.org/10.1177/1362361317745857.

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Both sleep problems and unemployment are common in adults with autism spectrum disorder; however, little research has explored this relationship in this population. This study aimed to explore factors that may be associated with the presence of an International Classification of Sleep Disorders–Third Edition defined sleep disorder in adults with autism spectrum disorder (IQ > 80). A total of 36 adults with autism spectrum disorder and 36 controls were included in the study. Participants completed a 14-day actigraphy assessment and questionnaire battery. Overall, 20 adults with autism spectrum disorder met the International Classification of Sleep Disorders–Third Edition criteria for insomnia and/or a circadian rhythm sleep-wake disorder, while only 4 controls met criteria for these disorders. Adults with autism spectrum disorder and an International Classification of Sleep Disorders–Third Edition sleep disorder had higher scores on the Pittsburgh Sleep Quality Index and were more likely to be unemployed compared to adults with autism spectrum disorder and no sleep disorder. The findings demonstrate, for the first time, that sleep problems are associated with unemployment in adults with autism spectrum disorder. Further research exploring the direction of this effect is required; sleep problems that have developed during adolescence make attainment of employment for those with autism spectrum disorder difficult, or unemployment results in less restrictions required for optimal and appropriate sleep timing.

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Baird, Gillian, and Courtenay Frazier Norbury. "Social (pragmatic) communication disorders and autism spectrum disorder." Archives of Disease in Childhood 101, no. 8 (December 23, 2015): 745–51. http://dx.doi.org/10.1136/archdischild-2014-306944.

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Changes have been made to the diagnostic criteria for autism spectrum disorder (ASD) in the recent revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and similar changes are likely in the WHO International Classification of Diseases (ICD-11) due in 2017. In light of these changes, a new clinical disorder, social (pragmatic) communication disorder (SPCD), was added to the neurodevelopmental disorders section of DSM-5. This article describes the key features of ASD, SPCD and the draft ICD-11 approach to pragmatic language impairment, highlighting points of overlap between the disorders and criteria for differential diagnosis.

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Kyriakopoulos, M. "Psychosis and Autism Spectrum Disorders." European Psychiatry 41, S1 (April 2017): S45. http://dx.doi.org/10.1016/j.eurpsy.2017.01.198.

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Autism spectrum disorders (ASD) and schizophrenia were separated into different diagnostic categories in the late 1970's (DSM-III) having previously been considered as related diagnostic entities. Since then, several lines of evidence have indicated that these disorders show clinical and cognitive overlaps as well as some common neurobiological characteristics. Furthermore, there is a group of patients presenting with ASD and psychotic experiences who pose particular diagnostic and management challenges and may represent a subgroup of ASD more closely linked to psychosis. Evidence from a study of the first empirically derived classification of children with ASD in relation to psychosis based on three underlying symptom dimensions, anxiety, social deﬁcits and thought disorder, will be presented. Further phenomenological, genetic and neuroimaging research on the clinical boundaries and overlapping pathophysiology of ASD and psychosis may help better define their relationship and lead to more effective interventions. Understanding this relationship will also provide a framework of working with patients with mixed clinical presentations.Disclosure of interestThe author declares that he has no competing interest.

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Woodbury-Smith, Marc. "Changing diagnostic practices: autism spectrum disorder." Advances in Psychiatric Treatment 20, no. 1 (January 2014): 23–26. http://dx.doi.org/10.1192/apt.bp.112.010801.

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SummaryIn medical practice it is crucial that symptom descriptions are as precise and objective as possible, which psychiatry attempts to achieve through its psychopathological lexicon. The term ‘autism spectrum disorder’ has now entered psychiatric nosology, but the symptom definitions on which it is based are not robust, potentially making reliable and valid diagnoses a problem. This is further compounded by the spectral nature of the disorder and its lack of clear diagnostic boundaries. To overcome this, there is a need for a psychopathological lexicon of 'social cognition’ and a classification system that splits rather than lumps disorders with core difficulties in social interaction.

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Popovic-Deusic, Smiljka, Milica Pejovic-Milovancevic, Saveta Draganic-Gajic, Olivera Aleksic-Hil, and Dusica Lecic-Tosevski. "Psychotic spectrum disorders in childhood." Srpski arhiv za celokupno lekarstvo 136, no. 9-10 (2008): 555–58. http://dx.doi.org/10.2298/sarh0810555p.

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For a long time, there was a strong belief of existing continuity between childhood-onset psychoses and adult psychoses. Important moment in understanding psychotic presentations during infancy and childhood is Kanner's description of early infantile autism. Later studies of Rutter and Kolvin, as well as new classification systems, have delineated pervasive developmental disorders from all other psychotic disorders in childhood. But clinical experience is showing that in spite of existence of the group of pervasive developmental disorders with subgroups within it and necessary diagnostic criteria there are children with pervasive symptoms, who are not fulfilling all necessary diagnostic criteria for pervasive developmental disorder. Therefore, in this paper we are discussing and pointing at psychotic spectrum presentations in children, which have not the right place in any existing classification system (ICD-10, DSM-IV).

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Stankovic, Miodrag, Aneta Lakic, and Neda Ilic. "Autism and autistic spectrum disorders in the context of new DSM-V classification, and clinical and epidemiological data." Srpski arhiv za celokupno lekarstvo 140, no. 3-4 (2012): 236–43. http://dx.doi.org/10.2298/sarh1204236s.

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Autism is one of disorders from the autism spectrum, besides Asperger syndrome, atypical autism and pervasive developmental disorder not otherwise specified. They are classified as mental disorders as being manifested by a wide range of cognitive, emotional and neurobehavioural abnormalities. Key categorical characteristics of the disorder are clear impairments of the development of the child?s socialisation, understanding and production of verbal and non-verbal communication and restricted and repetitive patterns of behaviour. Demarcation boundaries are not clear, neither within the very group of the disorders from the autistic spectrum, nor with respect to the autistic behavioural features in the general population. For this reason, the term spectrum points out the significance of the dimensional assessment of autistic disorders, which will most likely be the basis of the new diagnostic classification of the disorders belonging to the current group of pervasive developmental disorders in the new DSM-V classification. The understanding, as well as the prevalence of the autistic spectrum disorders has changed drastically in the last four decades. From the previous 4 per 10,000 people, today?s prevalence estimates range from 0.6 to around 1%, and the increase of prevalence cannot be explained solely by better recognition on the part of experts and parents or by wider diagnostic criteria. The general conclusion is that the autistic spectrum disorders are no longer rare conditions and that the approach aimed at acknowledging the warning that this is an urgent public health problem is completely justified.

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Kuznetsova, L., M. Brychuk, L. Pogasiy, and K. Zhizhkun. "Features of the influence of playing activities on preschool children with a spectrum of autistic disorders in the process of adaptive physical education." Scientific Journal of National Pedagogical Dragomanov University. Series 15. Scientific and pedagogical problems of physical culture (physical culture and sports), no. 1(121) (January 29, 2020): 53–59. http://dx.doi.org/10.31392/npu-nc.series15.2019.1(121)20.10.

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The article deals with the peculiarities of the mental development of children with autism spectrum disorders, their psychophysical abilities, the formation of cognitive functions, the means of communication, the development of the emotional-volitional sphere, behavior in society. Features of correctional and pedagogical work with autistic children at the present stage are considered. A detailed definition of the definition of "autism", a modern classification of autism, the main features of autistic disorders in all its clinical variants are presented. Statistics on the incidence of autism in the world are provided. The characteristics and peculiarities of psychomotor development in preschool children with autism spectrum disorders and the logic of psychomotor development, the features of psychomotor development, the offered educational and correction tasks are presented. Importance and place of mobile games as the main means of adaptive physical education of preschool children with this nosology have been determined. Mobility games are distributed in the focus on the development of motor skills of preschool children with autism spectrum disorders. A modified classification of mobile games, entertainment, and entertainment that can be used in adaptive physical education and extracurricular forms of preschool-age children with autism spectrum disorders is presented.

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Vohra, Rini, Suresh Madhavan, and Usha Sambamoorthi. "Comorbidity prevalence, healthcare utilization, and expenditures of Medicaid enrolled adults with autism spectrum disorders." Autism 21, no. 8 (October 20, 2016): 995–1009. http://dx.doi.org/10.1177/1362361316665222.

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A retrospective data analysis using 2000–2008 three state Medicaid Analytic eXtract was conducted to examine the prevalence and association of comorbidities (psychiatric and non-psychiatric) with healthcare utilization and expenditures of fee-for-service enrolled adults (22–64 years) with and without autism spectrum disorders (International Classification of Diseases, Ninth Revision–clinical modification code: 299.xx). Autism spectrum disorder cases were 1:3 matched to no autism spectrum disorder controls by age, gender, and race using propensity scores. Study outcomes were all-cause healthcare utilization (outpatient office visits, inpatient hospitalizations, emergency room, and prescription drug use) and associated healthcare expenditures. Bivariate analyses (chi-square tests and t-tests), multinomial logistic regressions (healthcare utilization), and generalized linear models with gamma distribution (expenditures) were used. Adults with autism spectrum disorders (n = 1772) had significantly higher rates of psychiatric comorbidity (81%), epilepsy (22%), infections (22%), skin disorders (21%), and hearing impairments (18%). Adults with autism spectrum disorders had higher mean annual outpatient office visits (32ASD vs 8noASD) and prescription drug use claims (51ASD vs 24noASD) as well as higher mean annual outpatient office visits (US$4375ASD vs US$824noASD), emergency room (US$15,929ASD vs US$2598noASD), prescription drug use (US$6067ASD vs US$3144noASD), and total expenditures (US$13,700ASD vs US$8560noASD). The presence of a psychiatric and a non-psychiatric comorbidity among adults with autism spectrum disorders increased the annual total expenditures by US$4952 and US$5084, respectively.

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Bachmann, Christian J., Bettina Gerste, and Falk Hoffmann. "Diagnoses of autism spectrum disorders in Germany: Time trends in administrative prevalence and diagnostic stability." Autism 22, no. 3 (December 20, 2016): 283–90. http://dx.doi.org/10.1177/1362361316673977.

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For Germany, no data on trends in autism spectrum disorder diagnoses are available. The primary aim of this study was to establish the time trends in the administrative prevalence of autism spectrum disorder diagnoses. The second aim was to assess the stability of autism spectrum disorder diagnoses over time. We analysed administrative outpatient data (2006–2012) from a nationwide health insurance fund and calculated the prevalence of autism spectrum disorder diagnoses for each year, stratified by age and sex. Additionally, we studied a cohort with a first-time diagnosis of autism spectrum disorder in 2007 through 2012, investigating the percentage of retained autism spectrum disorder diagnoses. From 2006 to 2012, the prevalence of autism spectrum disorder diagnoses in 0- to 24-year-olds increased from 0.22% to 0.38%. In insurees with a first-time autism spectrum disorder diagnosis in 2007, this diagnosis was carried on in all years through 2012 in 33.0% (The International Classification of Diseases, Tenth Revision diagnoses: F84.0/F84.1/F84.5) and 11.2% (F84.8/F84.9), respectively. In Germany, like in other countries, there has been an increase in the administrative prevalence of autism spectrum disorder diagnoses. Yet, prevalences are still lower than in some other Western countries. The marked percentage of autism spectrum disorder diagnoses which were not retained could indicate a significant portion of autism spectrum disorder misdiagnoses, which might contribute to rising autism spectrum disorder prevalences.

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Tkachuk, E. A. "Using the syndrome approach to the diagnosis of autism in children." Meditsinskiy sovet = Medical Council, no. 12 (July 12, 2022): 200–204. http://dx.doi.org/10.21518/2079-701x-2022-16-12-200-204.

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Autism spectrum disorders in children is a very urgent problem. Today, there is an increase in the number of children suffering from autism spectrum disorders around the world. The relevance of early diagnosis of autism spectrum disorders for timely treatment and correction is high. However, the diagnostic criteria for ASD require an assessment of certain psychomotor skills in a child, which mature much later than necessary for a timely diagnosis, so the diagnosis is made by 5-7 years. New clinical guidelines suggest that primary screening should be performed by a pediatrician. However, practice shows that in reality this does not happen. The reason for this is not only a contradiction in the diagnostic criteria, but also the current ICD-10 classification, which does not reflect the pathogenetic processes in the child's body. Despite this, new clinical guidelines recognize the role of genetic disorders and epigenetic factors in the development of autism spectrum disorders. In this regard, it is proposed to consider autism spectrum disorders as a syndrome that accompanies various genetic disorders, both chromosomal and monogenic anomalies. This approach enables early diagnosis of autism spectrum disorders, as well as the development of treatment and correction methods based on pathogenetic disorders. Currently, more than 100 genes associated with autism are known. In the above studies, it was noted that the polygenic nature of disorders in autism does not allow focusing on phenotypic features. Probably, this is the reason for the difficulties in diagnosing autism by external signs and the low efficiency of the currently known screening methods for diagnosing autism spectrum disorders. Therefore, autism spectrum disorders must be considered from the point of view of pathogenetic changes in the child's body, usually of a hereditary nature, which will allow us to offer effective methods of diagnosis, treatment and correction.

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Ganesan, Srividhya, Raju Dr., and Dr Senthil J. "Prediction of Autism Spectrum Disorder by Facial Recognition Using Machine Learning." Webology 18, no. 02 (September 28, 2021): 406–17. http://dx.doi.org/10.14704/web/v18si02/web18291.

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Autism is normally characterized as pervading disorder. The role Pervasive implies that the disorder is acute. Autism spectrum disorder (ASD) individuals face difficulties in interacting with others. They also have a problem in responding to the actions, hyperactive and behavioural issues. There have been numerous technological enhancements in prediction of autism traits. This paper focusses on various machine learning methods to classify an autistic child. It mainly focusses on classification models applying VGG16 algorithm of SVM classifier, CNN and Haar Cascade using OpenCV. Using these models, better accuracy was achieved compared to other models of classification.

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Lahuis, B. E., S. Durston, H. Nederveen, M. Zeegers, S. J. M. C. Palmen, and H. Van Engeland. "MRI-based morphometry in children with multiple complex developmental disorder, a phenotypically defined subtype of pervasive developmental disorder not otherwise specified." Psychological Medicine 38, no. 9 (September 10, 2007): 1361–67. http://dx.doi.org/10.1017/s0033291707001481.

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BackgroundThe DSM-IV-R classification Pervasive Developmental Disorder – Not otherwise Specified (PDD-NOS) is based on the symptoms for autism and includes a wide variety of phenotypes that do not meet full criteria for autism. As such, PDD-NOS is a broad and poorly defined residual category of the autism spectrum disorders. In order to address the heterogeneity in this residual category it may be helpful to define clinical and neurobiological subtypes. Multiple complex developmental disorder (MCDD) may constitute such a subtype. In order to study the neurobiological specificity of MCDD in comparison with other autism spectrum disorders, we investigated brain morphology in children (age 7–15 years) with MCDD compared to children with autism and typically developing controls.MethodStructural MRI measures were compared between 22 high-functioning subjects with MCDD and 21 high-functioning subjects with autism, and 21 matched controls.ResultsSubjects with MCDD showed an enlarged cerebellum and a trend towards larger grey-matter volume compared to control subjects. Compared to subjects with autism, subjects with MCDD had smaller intracranial volume.ConclusionsWe report a pattern of volumetric changes in the brains of subjects with MCDD, similar to that seen in autism. However, no enlargement in head size was found. This suggests that although some of the neurobiological changes associated with MCDD overlap with those in autism, others do not. These neurobiological changes may reflect differences in the developmental trajectories associated with these two subtypes of autism spectrum disorders.

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Pavithra, D., and A. N. Jayanthi. "An Enhanced Deep Recurrent Neural Network for Autism Spectrum Disorder Diagnosis." Journal of Medical Imaging and Health Informatics 11, no. 12 (December 1, 2021): 3028–37. http://dx.doi.org/10.1166/jmihi.2021.3893.

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Autism Spectrum Disorder is one of the major investigation area in current era. There are many research works introduced earlier for handling the Autism Spectrum Disorders. However those research works doesn’t achieve the expected accuracy level. The accuracy and prediction efficiency can be increased by building a better classification system using Deep Learning. This paper focuses on the deep learning technique for Autism Diagnosis and the domain identification. In the proposed work, an Enhanced Deep Recurrent Neural Network has been developed for the detection of ASD at all ages. It attempts to predict the autism spectrum in the children along with prediction of areas which can predict the autism in the prior level. The main advantage of EDRNN is to provide higher accuracy in classification and domain identification. Here Artificial Algal Algorithm is used for identifying the most relevant features from the existing feature set. This model was evaluated for the data that followed Indian Scale for Assessment of Autism. The results obtained for the proposed EDRNN has better accuracy, sensitivity, specificity, recall and precision.

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Nurfalah, Ridan, Sri Rahayu, and Muhammad Faittullah Akbar. "The Analysis of Adult Autism Spectrum Disorders Screening Using Neural Network." SinkrOn 4, no. 1 (October 16, 2019): 196. http://dx.doi.org/10.33395/sinkron.v4i1.10148.

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One of the increasing developmental disorders in Indonesia is Autism Spectrum Disorder (ASD), developmental disorder characterized by difficulties to conduct verbal and non-verbal communication and social interaction. This disorder cannot be tolerated and requires early treatment to reduce its development. However, ASD treatments required ineffective treatment costs and waiting times diagnosis were lengthly. One effective alternative diagnosis isto use the screening technology to determine the early symptoms of ASD disorders. The rapid development of the number of ASD cases around the world required researchers to determine a dataset with behavioral properties to update the screening process. Thus, the purpose of this study is to predict the success of screening performed on adults with Autism Spectrum Disorder (ASD) using the researchers’ results dataset, so that the dataset could be used as a benchmark for the success of the ASD screening process. The method used is machine learning neural network method with 100 training cycle, learning rate 0,01 and momentum 0,9 resulted in a classification accuracy of 96.00%

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Stepkova, Oksana Vasilievna, and Natalia Vasilievna Kushnareva. "The Information Site as the Way of the Communicative Skills Development of Children with Autism Spectrum Disorder." Siberian Pedagogical Journal, no. 3 (July 7, 2021): 90–97. http://dx.doi.org/10.15293/1813-4718.2103.09.

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Introduction. The article is devoted to the problem of the communicative skills development of primary school children with autism spectrum disorders using the information site. The purpose of the article is to highlight the possibilities of using the information site by teachers in the process of communication skills development in children with autism spectrum disorders. Research methodology and methods. The research is based on the methodology of a differentiated approach taking into account the age and individual capabilities of the schoolchildren, as well as the severity of the disorder. Various classifications of autism spectrum disorders have been analyzed, and special attention has been paid to the consideration of the peculiarities of speech development, namely, its communicative function. The stages of the research carried out in order to identify the level of communication skills development of children with autism spectrum disorders and the search for ways of corrective work are reflected. Research results. The results of experimental work with the use of an information site aimed to communication skills development of primary schoolchildren with autism spectrum disorders have been presented. Conclusion. In conclusion we should underline that the communicative skills are one of the main manifestations of autism spectrum disorders and teachers can use information technology (for example, an information site) to develop these skills.

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Viljoen, Marisa, Soheil Mahdi, David Griessel, Sven Bölte, and Petrus J. de Vries. "Parent/caregiver perspectives of functioning in autism spectrum disorders: A comparative study in Sweden and South Africa." Autism 23, no. 8 (May 2, 2019): 2112–30. http://dx.doi.org/10.1177/1362361319829868.

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Functional outcomes in autism spectrum disorder can be highly variable given the heterogeneous nature of autism spectrum disorder and its interaction with environmental factors. We set out to compare parent/caregiver perceptions of functioning in two divergent countries that participated in the International Classification of Functioning Disability and Health (ICF) Core Set for Autism Spectrum Disorder development study. We focused on the frequency and content of items reported, and hypothesized that environmental factors would most frequently be reported as barriers to functioning in low-resource settings. Using frequency and qualitative content analysis, we compared data from South Africa ( n = 22) and Sweden ( n = 13). Frequency agreement was seen in three activities and participation categories, and one environmental factor. Obvious frequency differences were observed in one environmental factors category, six body functions categories and three activities and participation categories. Only three ICF categories (immediate family, attention functions, products and technology for personal use) differed in content. Contrary to our hypotheses, few differences in perspectives about environmental factors emerged. The universality of our findings supports the global usefulness of the recently developed ICF Core Sets for Autism Spectrum Disorder. We recommend that more comparative studies on autism spectrum disorder and functioning should be conducted, and that similar comparisons in other disorders where Core Sets have been developed may be valuable.

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Saleh, Abdulrazak Yahya, and Lim Huey Chern. "Autism Spectrum Disorder Classification Using Deep Learning." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 08 (August 16, 2021): 103. http://dx.doi.org/10.3991/ijoe.v17i08.24603.

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<p class="0abstract">The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. ASD is a developmental disability that causes the affected people to have significant communication, social, and behavioural challenges. People with autism are saddled with communication problems, difficulties in social interaction and displaying repetitive behaviours. Several methods have been used to classify the ASD from non-ASD people. However, there is a need to explore more algorithms that can yield better classification performance. Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. These artificial intelligence tasks refer to object detection, speech recognition, and machine translation. In this research, the convolutional neural network (CNN) is employed. This algorithm is used to find processes that can classify ASD with a higher level of accuracy. The image data is pre-processed; the CNN algorithm is then applied to classify the ASD and non-ASD, and the steps of implementing the CNN algorithm are clearly stated. Finally, the effectiveness of the algorithm is evaluated based on the accuracy performance. The support vector machine (SVM) is utilised for the purpose of comparison. The CNN algorithm produces better results with an accuracy of 97.07%, compared with the SVM algorithm. In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.</p>

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Barrios-Fernández, Sabina, Margarita Gozalo, Beatriz Díaz-González, and Andrés García-Gómez. "A Complementary Sensory Tool for Children with Autism Spectrum Disorders." Children 7, no. 11 (November 20, 2020): 244. http://dx.doi.org/10.3390/children7110244.

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Background: Sensory integration (SI) issues are widely described in people with autism spectrum disorder (ASD), impacting in their daily life and occupations. To improve their quality of life and occupational performance, we need to improve clinical and educational evaluation and intervention processes. We aim to develop a tool for measuring SI issues for Spanish children and adolescents with ASD diagnosis, to be used as a complementary tool to complete the Rivière’s Autism Spectrum Inventory, a widely used instrument in Spanish speaking places to describe the severity of ASD symptoms, recently updated with a new sensory scale with three dimensions. Methods: 458 Spanish participants complemented the new questionnaire, initially formed by 73 items with a 1–5 Likert scale. Results: The instrument finally was composed of 41 items grouped in three factors: modulation disorders (13 items), discrimination disorders (13 items), and sensory-based motor disorders (15 items). The goodness-of-fit indices from factor analyses, reliability, and the analysis of the questionnaire’s classification capability offered good values. Conclusions: The new questionnaire shows good psychometric properties and seems to be a good complementary tool to complete new the sensory scale in the Rivière’s Autism Spectrum Inventory.

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Sharma, Samata R., Xenia Gonda, and Frank I. Tarazi. "Autism Spectrum Disorder: Classification, diagnosis and therapy." Pharmacology & Therapeutics 190 (October 2018): 91–104. http://dx.doi.org/10.1016/j.pharmthera.2018.05.007.

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Fabiano, Diego, Shaun Canavan, Heather Agazzi, Saurabh Hinduja, and Dmitry Goldgof. "Gaze-based classification of autism spectrum disorder." Pattern Recognition Letters 135 (July 2020): 204–12. http://dx.doi.org/10.1016/j.patrec.2020.04.028.

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Ustinova, Nataliya V., and Leyla S. Namazova-Baranova. "Role of Pediatrician in Early Risk Evaluation, Diagnosis and Management of Children with Autism Spectrum Disorders." Current Pediatrics 20, no. 2 (May 18, 2021): 116–21. http://dx.doi.org/10.15690/vsp.v20i2.2255.

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The article discusses recent ideas about autism: classification approaches, incidence, etiology and pathogenesis, clinical manifestations and diagnosis, comorbid medical conditions, early detection approaches and medical care for children with autism spectrum disorders. The focus is on the information needed for pediatricians in their practice to provide effective medical care for children with neurodevelopmental disorders.

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Thabtah, Fadi, and David Peebles. "A new machine learning model based on induction of rules for autism detection." Health Informatics Journal 26, no. 1 (January 29, 2019): 264–86. http://dx.doi.org/10.1177/1460458218824711.

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Autism spectrum disorder is a developmental disorder that describes certain challenges associated with communication (verbal and non-verbal), social skills, and repetitive behaviors. Typically, autism spectrum disorder is diagnosed in a clinical environment by licensed specialists using procedures which can be lengthy and cost-ineffective. Therefore, scholars in the medical, psychology, and applied behavioral science fields have in recent decades developed screening methods such as the Autism Spectrum Quotient and Modified Checklist for Autism in Toddlers for diagnosing autism and other pervasive developmental disorders. The accuracy and efficiency of these screening methods rely primarily on the experience and knowledge of the user, as well as the items designed in the screening method. One promising direction to improve the accuracy and efficiency of autism spectrum disorder detection is to build classification systems using intelligent technologies such as machine learning. Machine learning offers advanced techniques that construct automated classifiers that can be exploited by users and clinicians to significantly improve sensitivity, specificity, accuracy, and efficiency in diagnostic discovery. This article proposes a new machine learning method called Rules-Machine Learning that not only detects autistic traits of cases and controls but also offers users knowledge bases (rules) that can be utilized by domain experts in understanding the reasons behind the classification. Empirical results on three data sets related to children, adolescents, and adults show that Rules-Machine Learning offers classifiers with higher predictive accuracy, sensitivity, harmonic mean, and specificity than those of other machine learning approaches such as Boosting, Bagging, decision trees, and rule induction.

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Solov'eva, N. V., Ya V. Kuvshinova, I. V. Kichuk, S. V. Chausova, V. B. Vil'yanov, and S. A. Kremenitskaya. "Dichotomous classification of autism spectrum disorders: syndromal and non-syndromal forms." Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova 118, no. 4 (2018): 107. http://dx.doi.org/10.17116/jnevro201811841107-112.

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Gerner, TM, J. Brar, ML Kalbfleisch, and JW VanMeter. "Classification of Subtypes in a Pediatric Sample with Autism Spectrum Disorders." NeuroImage 47 (July 2009): S45. http://dx.doi.org/10.1016/s1053-8119(09)70054-0.

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Lord, Catherine, and Rebecca M. Jones. "Annual Research Review: Re-thinking the classification of autism spectrum disorders." Journal of Child Psychology and Psychiatry 53, no. 5 (April 4, 2012): 490–509. http://dx.doi.org/10.1111/j.1469-7610.2012.02547.x.

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Choi, Eun Soo, Hee Jeong Yoo, Min Soo Kang, and Soon Ae Kim. "Applying Artificial Intelligence for Diagnostic Classification of Korean Autism Spectrum Disorder." Psychiatry Investigation 17, no. 11 (November 25, 2020): 1090–95. http://dx.doi.org/10.30773/pi.2020.0211.

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Objective The primary objective of this study was to predict subgroups of autism spectrum disorder (ASD) based on the Diagnostic Statistical Manual for Mental Disorders-IV Text Revision (DSM-IV-TR) by machine learning (ML). The secondary objective was to set up a ranking of Autism Diagnostic Interview-Revised (ADI-R) diagnostic algorithm items based on ML, and to confirm whether ML can sufficiently predict the diagnosis with these minimum items.Methods In the first experiment, a multiclass decision forest algorithm was applied, and the diagnostic algorithm score value of 1,269 Korean ADI-R test data was used for prediction. In the second experiment, we used 539 Korean ADI-R case data (over 48 months with verbal language) to apply mutual information to rank items used in the ADI diagnostic algorithm.Results In the first experiment, the results of predicting in the case of pervasive developmental disorder not otherwise specified as “ASD” were almost three times higher than predicting it as “No diagnosis.” In the second experiment, the top 10 ranking items of ADI-R were mainly related to the quality abnormality of communication.Conclusion In conclusion, we verified the applicability of ML in diagnosis and found that the application of artificial intelligence for rapid diagnosis or screening of ASD patients may be useful.

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Bogdashina, O. B. "Synaesthesia in Autism." Autism and Developmental Disorders 14, no. 3 (2016): 21–31. http://dx.doi.org/10.17759/autdd.2016140302.

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Synaesthesia — a phenomenon of perception, when stimulation of one sensory modality triggers a perception in one or more other sensory modalities. Synaesthesia is not uniform and can manifest itself in different ways. As the sensations and their interpretation vary in different periods of time, it makes it hard to study this phenom¬enon. The article presents the classification of different forms of synaesthesia, including sensory and cognitive; and bimodal and multimodal synaesthesia. Some synaesthetes have several forms and variants of synaesthesia, while others – just one form of it. Although synaesthesia is not specific to autism spectrum disorders, it is quite common among autistic individuals. The article deals with the most common forms of synaesthesia in autism, advantages and problems of synesthetic perception in children with autism spectrum disorders, and provides some advice to parents how to recognise synaesthesia in children with autism.

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Bölte, Sven, Soheil Mahdi, Petrus J. de Vries, Mats Granlund, John E. Robison, Cory Shulman, Susan Swedo, et al. "The Gestalt of functioning in autism spectrum disorder: Results of the international conference to develop final consensus International Classification of Functioning, Disability and Health core sets." Autism 23, no. 2 (January 29, 2018): 449–67. http://dx.doi.org/10.1177/1362361318755522.

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Autism spectrum disorder is associated with diverse social, educational, and occupational challenges. To date, no standardized, internationally accepted tools exist to assess autism spectrum disorder–related functioning. World Health Organization’s International Classification of Functioning, Disability and Health can serve as foundation for developing such tools. This study aimed to identify a comprehensive, a common brief, and three age-appropriate brief autism spectrum disorder Core Sets. Four international preparatory studies yielded in total 164 second-level International Classification of Functioning, Disability and Health candidate categories. Based on this evidence, 20 international autism spectrum disorder experts applied an established iterative decision-making consensus process to select from the candidate categories the most relevant ones to constitute the autism spectrum disorder Core Sets. The consensus process generated 111 second-level International Classification of Functioning, Disability and Health categories in the Comprehensive Core Set for autism spectrum disorder—one body structure, 20 body functions, 59 activities and participation categories, and 31 environmental factors. The Common Brief Core Set comprised 60 categories, while the age-appropriate core sets included 73 categories in the preschool version (0- to 5-year-old children), 81 in the school-age version (6- to 16-year-old children and adolescents), and 79 in the older adolescent and adult version (⩾17-year-old individuals). The autism spectrum disorder Core Sets mark a milestone toward the standardized assessment of autism spectrum disorder–related functioning in educational, administrative, clinical, and research settings.

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Scott, Melissa, Ben Milbourn, Marita Falkmer, Melissa Black, Sven Bӧlte, Alycia Halladay, Matthew Lerner, Julie Lounds Taylor, and Sonya Girdler. "Factors impacting employment for people with autism spectrum disorder: A scoping review." Autism 23, no. 4 (August 3, 2018): 869–901. http://dx.doi.org/10.1177/1362361318787789.

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The aim of this study is to holistically synthesise the extent and range of literature relating to the employment of individuals with autism spectrum disorder. Database searches of Medline, CINAHL, PsychINFO, Scopus, ERIC, Web of Science and EMBASE were conducted. Studies describing adults with autism spectrum disorder employed in competitive, supported or sheltered employment were included. Content analysis was used to identify the strengths and abilities in the workplace of employees with autism spectrum disorder. Finally, meaningful concepts relating to employment interventions were extracted and linked to the International Classification of Functioning, Disability and Health Core Sets for autism spectrum disorder. The search identified 134 studies for inclusion with methodological quality ranging from limited to strong. Of these studies, only 36 evaluated employment interventions that were coded and linked to the International Classification of Functioning, Disability and Health, primarily focusing on modifying autism spectrum disorder characteristics for improved job performance, with little consideration of the impact of contextual factors on work participation. The International Classification of Functioning, Disability and Health Core Sets for autism spectrum disorder are a useful tool in holistically examining the employment literature for individuals with autism spectrum disorder. This review highlighted the key role that environmental factors play as barriers and facilitators in the employment of people with autism spectrum disorder and the critical need for interventions which target contextual factors if employment outcomes are to be improved.

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Ward, Samantha L., Karen A. Sullivan, and Linda Gilmore. "Agreement Between a Brief Autism Observational Instrument and Established ASD Measures." Educational and Developmental Psychologist 33, no. 2 (May 20, 2016): 127–38. http://dx.doi.org/10.1017/edp.2016.1.

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Objective: Limited time and resources necessitate the availability of accurate, inexpensive and rapid diagnostic aids for Autism Spectrum Disorder (ASD). The Autistic Behavioural Indicators Instrument (ABII) was developed for this purpose, but its psychometric properties have not yet been fully established. Method: The clinician-rated ABII, the Autism Diagnostic Observation Schedule (ADOS), the Childhood Autism Rating Scale – Second Edition, Standard Version (CARS2-ST), and Diagnostic and Statistical Manual of Mental Disorders (DSM-5) diagnostic criteria were individually administered to children with an independent paediatrician DSM-IV-TR or DSM-5 autism spectrum diagnosis, aged 2-6 years (n = 51, Mchildage = 3.6 years). The agreement between each of the measures on autism diagnostic classification was calculated and compared, and the intercorrelation between the instruments examined. Results: There was significant moderate agreement for the classification of autism between the ABII and the DSM-5, and significant fair agreement between the ABII and ADOS and ABII and CARS2-ST. True positive diagnostic classifications were similar across the ABII (n = 47, 92.2%) and ADOS (n = 45, 88.2%), and significantly higher than the CARS2-ST (n = 30, 58.8%). The ABII total scale score was strongly positively correlated with both the ADOS and CARS2-ST total scores. Conclusion: The ABII's test characteristics were comparable to those of established measures, and the intercorrelations between selected measures support its convergent validity. The ABII could be added to the clinician's toolbox as a screening test.

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Jayalakshmi, V. Jalaja. "A Hybrid Approach for Autism Spectrum Disorder Classification." Bioscience Biotechnology Research Communications 13, no. 11 (December 25, 2020): 10–14. http://dx.doi.org/10.21786/bbrc/13.11/3.

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Deepa, B., and K. S. Jeen Marseline. "Exploration of Autism Spectrum Disorder using Classification Algorithms." Procedia Computer Science 165 (2019): 143–50. http://dx.doi.org/10.1016/j.procs.2020.01.098.

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McGonigle-Chalmers, Margaret, and Ben Alderson-Day. "Free Classification as a Window on Executive Functioning in Autism Spectrum Disorders." Journal of Autism and Developmental Disorders 40, no. 7 (January 28, 2010): 844–57. http://dx.doi.org/10.1007/s10803-010-0947-5.

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Nagesh, Nagashree, Premjyoti Patil, Shantakumar Patil, and Mallikarjun Kokatanur. "An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1768. http://dx.doi.org/10.11591/ijece.v12i2.pp1768-1775.

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The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.

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Muris, Peter, and Thomas H. Ollendick. "Selective Mutism and Its Relations to Social Anxiety Disorder and Autism Spectrum Disorder." Clinical Child and Family Psychology Review 24, no. 2 (January 19, 2021): 294–325. http://dx.doi.org/10.1007/s10567-020-00342-0.

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AbstractIn current classification systems, selective mutism (SM) is included in the broad anxiety disorders category. Indeed, there is abundant evidence showing that anxiety, and social anxiety in particular, is a prominent feature of SM. In this article, we point out that autism spectrum problems in addition to anxiety problems are sometimes also implicated in SM. To build our case, we summarize evidence showing that SM, social anxiety disorder (SAD), and autism spectrum disorder (ASD) are allied clinical conditions and share communalities in the realm of social difficulties. Following this, we address the role of a prototypical class of ASD symptoms, restricted and repetitive behaviors and interests (RRBIs), which are hypothesized to play a special role in the preservation and exacerbation of social difficulties. We then substantiate our point that SM is sometimes more than an anxiety disorder by addressing its special link with ASD in more detail. Finally, we close by noting that the possible involvement of ASD in SM has a number of consequences for clinical practice with regard to its classification, assessment, and treatment of children with SM and highlight a number of directions for future research.

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Sorokin, A. B., E. Yu Davydova, L. V. Samarina, E. E. Ermolaeva, K. Yu Antokhina, E, Kuzembayeva, A. V. Khaustov, and O. Balandina. "Standardized Diagnostic Instruments for Autism Spectrum Disorders: the Use of ADOS-2 and ADI-R." Autism and Developmental Disorders 19, no. 1 (2021): 12–24. http://dx.doi.org/10.17759/autdd.2021190102.

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Standardized diagnostic methods for autism spectrum disorders (ASD) have been internationally used by professionals for diagnosis validation, diagnostic classification for intervention planning, structured collection of behavioral and developmental data as well as stand-alone diagnostic instruments. Recently, two of such instruments — Autism Diagnostic Observation Schedule ADOS-2 and Autism Diagnostic Interview ADI-R — became available in Russian. The article briefly describes both instruments and presents expert assessment of potential and possible limitations of Russian-language ADOS-2 and ADI-R. Preliminary ADOS-2 psychometric data attests to sufficient sensitivity and positive predictive value to be used as an observation instrument. More research is needed to confirm its differential diagnostic ability.

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Al-Hiyali, Mohammed Isam, Norashikin Yahya, Ibrahima Faye, and Ahmed Faeq Hussein. "Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network." Sensors 21, no. 16 (August 4, 2021): 5256. http://dx.doi.org/10.3390/s21165256.

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The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

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Drzazga-Lech, Maja, Monika Kłeczek, and Marta Ir. "Różne sposoby definiowania autyzmu. Przegląd stanowisk." Acta Universitatis Lodziensis. Folia Sociologica, no. 79 (December 30, 2021): 49–62. http://dx.doi.org/10.18778/0208-600x.79.03.

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Autyzm jest pojęciem wieloznacznym, nieostrym. W nomenklaturze medycznej kilkakrotnie już zmieniał się jego zakres semantyczny. W artykule przedstawiono sposoby występowania tego pojęcia w klasyfikacjach międzynarodowych DSM (Diagnostic and Statistical Manual of Mental Disorders) i ICD (International Statistical Classification of Diseases and Related Health Problems). Cechą wspólną tych definicji jest redukcjonistyczne podejście do pacjenta (jednostki zredukowanej do objawów chorobowych) i myślenie w kategoriach choroby bądź zaburzenia (ASD – Autism Spectrum Disorder). Obecnie istnieją również inne ujęcia autyzmu, o uznanie prawomocności których zabiegają aktorzy społeczni/grupy interesu spoza establishmentu medycznego. W opinii publicznej silnie zakorzenione jest skojarzenie autyzmu z puzzlem bądź kolorem niebieskim spopularyzowane przez fundację Autism Speaks. Ponadto w wydarzeniach medialnych, publikacjach o charakterze popularno-naukowym, naukowym, w tym w literaturze terapeutycznej, coraz częściej występuje określenie „stany ze spektrum autyzmu” (Autism Spectrum Condition). Ukazanie sporu o definicję autyzmu jest istotne, gdyż z argumentacji każdej ze stron wynikają implikacje w stosunku do zdrowia.

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Perez Torres, Lisset. "Disorders of the autistic spectrum: asperger syndrome and its repercussion in academic performance." Journal of America health 1, no. 2 (July 2, 2018): 22–38. http://dx.doi.org/10.37958/jah.v1i2.8.

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This research based on social, health and especially educational reality, involves us professionally in view of the fact that the majority of professional people, especially teachers, are not familiar with autism spectrum disorder, specifically with Asperger's Syndrome. and they may come to think that a child with an autistic ability behaves in a different way, and they find it difficult to interact with other classmates, therefore this may be difficult to understand conventional social rules and may seem of little importance for society, people with Asperger Syndrome (SA), in English Asperger syndrome (AS), have an average IQ and are likely to have teaching and learning problems like those who do not, however, have their learning needs They may be different from those of other children. Asperger's syndrome is a type of autism. Autism affects the way in which a person interprets the language, communicates and socializes. Until 2013, this syndrome used to be considered a condition in itself, with its own diagnosis. From that moment on, the guide used by doctors, the Diagnostic and Statistical Manual of Mental Disorders, commonly known as DSM-5, changed the classification of Asperger's syndrome.

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Erkan, Uğur, and Dang N. H. Thanh. "Autism Spectrum Disorder Detection with Machine Learning Methods." Current Psychiatry Research and Reviews 15, no. 4 (January 15, 2020): 297–308. http://dx.doi.org/10.2174/2666082215666191111121115.

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Background: Autistic Spectrum Disorder (ASD) is a disorder associated with genetic and neurological components leading to difficulties in social interaction and communication. According to statistics of WHO, the number of patients diagnosed with ASD is gradually increasing. Most of the current studies focus on clinical diagnosis, data collection and brain images analysis, but do not focus on the diagnosis of ASD based on machine learning. Objective: This study aims to classify ASD data to provide a quick, accessible and easy way to support early diagnosis of ASD. Methods: Three ASD datasets are used for children, adolescences and adults. To classify the ASD data, we used the k-Nearest Neighbours method (kNN), the Support Vector Machine method (SVM) and the Random Forests method (RF). In our experiments, the data was randomly split into training and test sets. The parts of the data were randomly selected 100 times to test the classification methods. Results: The final results were assessed by the average values. It is shown that SVM and RF are effective methods for ASD classification. In particular, the RF method classified the data with an accuracy of 100% for all above datasets. Conclusion: The early diagnosis of ASD is critical. If the number of data samples is large enough, we can achieve a high accuracy for machine learning-based ASD diagnosis. Among three classification methods, RF achieves the best performance for ASD data classification.

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Andrade, Evandro, Samuel Portela, Plácido Rogério Pinheiro, Luciano Comin Nunes, Marum Simão Filho, Wagner Silva Costa, and Mirian Caliope Dantas Pinheiro. "A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis." Computational and Mathematical Methods in Medicine 2021 (March 30, 2021): 1–14. http://dx.doi.org/10.1155/2021/1628959.

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Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. However, the following are among the most used: the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), of the American Psychiatric Association; the Revised Autistic Diagnostic Observation Schedule (ADOS-R); the Autistic Diagnostic Interview (ADI); and the International Classification of Diseases, 10th edition (ICD-10), published by the World Health Organization (WHO) and adopted in Brazil by the Unified Health System (SUS). The application of machine learning models helps make the diagnostic process of Autism Spectrum Disorder more precise, reducing, in many cases, the number of criteria necessary for evaluation, denoting a form of attribute engineering (feature engineering) efficiency. This work proposes a hybrid approach based on machine learning algorithms’ composition to discover knowledge and concepts associated with the multicriteria method of decision support based on Verbal Decision Analysis to refine the results. Therefore, the study has the general objective of evaluating how the mentioned hybrid methodology proposal can make the protocol derived from ICD-10 more efficient, providing agility to diagnosing Autism Spectrum Disorder by observing a minor symptom. The study database covers thousands of cases of people who, once diagnosed, obtained government assistance in Brazil.

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Tang, Michelle, Pulkit Kumar, Hao Chen, and Abhinav Shrivastava. "Deep Multimodal Learning for the Diagnosis of Autism Spectrum Disorder." Journal of Imaging 6, no. 6 (June 10, 2020): 47. http://dx.doi.org/10.3390/jimaging6060047.

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Recent medical imaging technologies, specifically functional magnetic resonance imaging (fMRI), have advanced the diagnosis of neurological and neurodevelopmental disorders by allowing scientists and physicians to observe the activity within and between different regions of the brain. Deep learning methods have frequently been implemented to analyze images produced by such technologies and perform disease classification tasks; however, current state-of-the-art approaches do not take advantage of all the information offered by fMRI scans. In this paper, we propose a deep multimodal model that learns a joint representation from two types of connectomic data offered by fMRI scans. Incorporating two functional imaging modalities in an automated end-to-end autism diagnosis system will offer a more comprehensive picture of the neural activity, and thus allow for more accurate diagnoses. Our multimodal training strategy achieves a classification accuracy of 74% and a recall of 95%, as well as an F1 score of 0.805, and its overall performance is superior to using only one type of functional data.

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Hategan, Ana, James A. Bourgeois, and Jeremy Goldberg. "Aging with autism spectrum disorder: an emerging public health problem." International Psychogeriatrics 29, no. 4 (September 27, 2016): 695–97. http://dx.doi.org/10.1017/s1041610216001599.

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From 1943, when Leo Kanner originally described autism, and to the first objective criteria for “infantile autism” in DSM-III and the inclusion of Asperger's disorder in DSM-IV, the subsequent classification scheme for autistic disorders has led to a substantial change with the 2013 issuance of the DSM-5 by including subcategories into one umbrella diagnosis of autism spectrum disorder (ASD) (Baker, 2013). ASD is a lifelong neurodevelopmental disorder, characterized by social and communication impairments and restricted, stereotypical patterns of behavior (Baker, 2013). It is currently expected that most, or all of the actual cases of ASD, are identified in a timely way (i.e. in early childhood). However, there are many undiagnosed older adults who may have met the current diagnostic criteria for ASD as children, but never received such a diagnosis due to the fact it had yet to be established. In addition, some patients with relatively less impairing phenotypes may escape formal diagnosis in childhood, only to later be diagnosed in adulthood. Nevertheless, the first generation of diagnosed patients with ASD is now in old age. Many such ASD patients have needed family and institutional support for their lives subsequent to childhood diagnosis. Due to aging and death of their parents and other supportive figures leading to a loss of social structures, there is no better time than now for the medical community to act.

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Shihab, Ammar I., Faten A. Dawood, and Ali H. Kashmar. "Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis." Advances in Bioinformatics 2020 (January 7, 2020): 1–8. http://dx.doi.org/10.1155/2020/3407907.

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Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.

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Rashad, Aya, Fahima Maghraby, Mohamed Fouad, Yasmin Lashin, and Amr Badr. "ASSOCIATION RULES BASED CLASSIFICATION FOR AUTISM SPECTRUM DISORDER DETECTION." International Journal of Intelligent Computing and Information Sciences 18, no. 2 (April 1, 2018): 13–28. http://dx.doi.org/10.21608/ijicis.2018.30118.

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Vignesh, U., K. G. Suma, R. Elakya, C. Vinothini, and A. M. Senthil Kumar. "Classification Techniques for Behaviour study of Autism spectrum Disorder." Journal of Physics: Conference Series 1964, no. 3 (July 1, 2021): 032008. http://dx.doi.org/10.1088/1742-6596/1964/3/032008.

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Li, Genyuan, Olivia Lee, and Herschel Rabitz. "High efficiency classification of children with autism spectrum disorder." PLOS ONE 13, no. 2 (February 15, 2018): e0192867. http://dx.doi.org/10.1371/journal.pone.0192867.

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Park, Hyun Ok, and Juengeun Lee. "Developing a Classification System for Circumscribed Interests in Students with Autism Spectrum Disorders." Special Education Research 14, no. 1 (February 28, 2015): 175. http://dx.doi.org/10.18541/ser.2015.02.14.1.175.

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Ke, Fengkai, and Rui Yang. "Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model." IEEE Access 8 (2020): 216298–307. http://dx.doi.org/10.1109/access.2020.3038479.

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Akshoomoff, Natacha, Catherine Lord, Alan J. Lincoln, Rachel Y. Courchesne, Ruth A. Carper, Jeanne Townsend, and Eric Courchesne. "Outcome Classification of Preschool Children With Autism Spectrum Disorders Using MRI Brain Measures." Journal of the American Academy of Child & Adolescent Psychiatry 43, no. 3 (March 2004): 349–57. http://dx.doi.org/10.1097/00004583-200403000-00018.

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Journal articles on the topic 'Autism spectrum disorders – Classification'

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