Using Multilayer Fuzzy Cognitive Maps to diagnose Autism Spectrum Disorder

Abstract Autism Spectrum Disorder (ASD) is comprised of a group of heterogeneous neurodevelopmental conditions, typically characterized by a triad of symptoms consisting of (1) impaired communication, (2) restricted interests, and (3) repetitive and stereotypical behavior pattern. An accurate and early diagnosis of autism can provide the basis for an appropriate educational and treatment program. In this work, we propose a computational model using a Multilayer Fuzzy Cognitive Map (hereafter referred to as MFCM) based on standardized behavioral assessments diagnosing the ASD (MFCM-ASD). The two standards used in the model are: the Autism Diagnostic Observation Schedule, Second Edition (ADOS2), and the Autism Diagnostic Interview Revised (ADIR). The MFCM’s are a soft computing technique characterized by robust properties that make it an effective technique for medical decision support systems. For the evaluation of the MFCM-ASD model, we have used real datasets of diagnosed cases, so as to compare against other method/approaches. Initial experiments demonstrated that the proposed model outperforms conventional Fuzzy Cognitive Maps (FCMs) for ASD diagnosis. Our MFCM-ASD model serves as a diagnostic tool required to support the medical decisions when determining the correct diagnosis of Autism in children with different cognitive characteristics.

[1]  P. Ponce,et al.  Methodology to Weight Evaluation Areas from Autism Spectrum Disorder ADOS-G Test with Artificial Neural Networks and Taguchi Method , 2014 .

[2]  Ira L. Cohen,et al.  A neural network approach to the classification of autism , 1993, Journal of autism and developmental disorders.

[3]  Matthew S. Goodwin,et al.  Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises , 2014, Journal of Autism and Developmental Disorders.

[4]  Andreas S. Andreou,et al.  A framework for static and dynamic analysis of multi-layer fuzzy cognitive maps , 2017, Neurocomputing.

[5]  Aguilar Jose,et al.  The FCM Designer Tool , 2010 .

[7]  K. Papanikolaou,et al.  Using the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule-Generic for the Diagnosis of Autism Spectrum Disorders in a Greek Sample with a Wide Range of Intellectual Abilities , 2009, Journal of autism and developmental disorders.

[8]  Peter Savadjiev,et al.  Whole brain white matter connectivity analysis using machine learning: An application to autism , 2017, NeuroImage.

[9]  Kasper Kok,et al.  Fuzzy Cognitive Maps for futures studies—A methodological assessment of concepts and methods , 2014 .

[10]  Paolo Perego,et al.  Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities , 2015, Journal of autism and developmental disorders.

[11]  G. Dawson,et al.  Practice parameter: Screening and diagnosis of autism , 2000, Neurology.

[12]  A. Tamilarasi,et al.  Prediction of autistic disorder using neuro fuzzy system by applying ANN technique , 2008, International Journal of Developmental Neuroscience.

[13]  Boris A. Galitsky.  A computational simulation tool for training autistic reasoning about mental attitudes , 2013, Knowl. Based Syst..

[14]  Tony Charman,et al.  Measurement Issues: Screening and diagnostic instruments for autism spectrum disorders - lessons from research and practice. , 2013, Child and adolescent mental health.

[15]  José Aguilar,et al.  Multilayer Cognitive Maps in the Resolution of Problems using the FCM Designer Tool , 2016, Appl. Artif. Intell..

[16]  Todd F. DeLuca,et al.  Use of machine learning to shorten observation-based screening and diagnosis of autism , 2012, Translational Psychiatry.

[17]  Voula C. Georgopoulos,et al.  Fuzzy cognitive map architectures for medical decision support systems , 2008, Appl. Soft Comput..

[18]  C. Lord,et al.  Autism Diagnostic Observation Schedule , 2016 .

[19]  J. Iverson,et al.  The development of autism spectrum disorders: variability and causal complexity. , 2017, Wiley interdisciplinary reviews. Cognitive science.

[20]  Jose L. Salmeron,et al.  Learning Fuzzy Grey Cognitive Maps using Nonlinear Hebbian-based approach , 2012, Int. J. Approx. Reason..

[21]  Voula C. Georgopoulos,et al.  A fuzzy cognitive map approach to differential diagnosis of specific language impairment , 2003, Artif. Intell. Medicine.

[22]  Abdollah Amirkhani,et al.  A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications , 2017, Comput. Methods Programs Biomed..

[23]  B. Tonge,et al.  Using the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule with Young Children with Developmental Delay: Evaluating Diagnostic Validity , 2008, Journal of autism and developmental disorders.

[24]  Eleni S. Vergini,et al.  A new conception on the Fuzzy Cognitive Maps method , 2016 .

[25]  Chrysostomos D. Stylios,et al.  An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps , 2003, IEEE Transactions on Biomedical Engineering.

[26]  So Hyun Kim,et al.  Combining information from multiple sources for the diagnosis of autism spectrum disorders for toddlers and young preschoolers from 12 to 47 months of age. , 2012, Journal of child psychology and psychiatry, and allied disciplines.

[27]  M. Fakhoury.  Autistic spectrum disorders: A review of clinical features, theories and diagnosis , 2015, International Journal of Developmental Neuroscience.

[28]  S. Bölte,et al.  The added value of the combined use of the Autism Diagnostic Interview–Revised and the Autism Diagnostic Observation Schedule: Diagnostic validity in a clinical Swedish sample of toddlers and young preschoolers , 2015, Autism : the international journal of research and practice.

[29]  Peter P. Groumpos,et al.  Fuzzy Cognitive Maps: Basic Theories and Their Application to Complex Systems , 2010 .

[30]  Dimitrios K. Iakovidis,et al.  Intuitionistic Fuzzy Cognitive Maps , 2013, IEEE Transactions on Fuzzy Systems.

[31]  Eric Courchesne,et al.  Outcome classification of preschool children with autism spectrum disorders using MRI brain measures. , 2004, Journal of the American Academy of Child and Adolescent Psychiatry.

[32]  Chrysostomos D. Stylios,et al.  Fuzzy Cognitive Maps , 2008 .

[33]  B. Leventhal,et al.  The Autism Diagnostic Observation Schedule—Generic: A Standard Measure of Social and Communication Deficits Associated with the Spectrum of Autism , 2000, Journal of autism and developmental disorders.

[34]  C. Johnson,et al.  Identification and Evaluation of Children With Autism Spectrum Disorders , 2007, Pediatrics.

[35]  Wojciech Froelich,et al.  Towards improving the efficiency of the fuzzy cognitive map classifier , 2017, Neurocomputing.

[36]  Elpiniki I. Papageorgiou,et al.  Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification , 2012, Appl. Soft Comput..

[37]  Elpiniki I. Papageorgiou,et al.  Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder , 2011, Expert Syst. Appl..

[38]  L. Parisi,et al.  The child with Autism Spectrum Disorders (ASDS): Behavioral and neurobiological aspects , 2015 .

[39]  Matthew W State,et al.  Autism spectrum disorders: from genes to neurobiology , 2015, Current Opinion in Neurobiology.

[40]  Matthew S. Goodwin,et al.  Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion. , 2016, Journal of child psychology and psychiatry, and allied disciplines.

[41]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[42]  D. Wall,et al.  Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism , 2012, PloS one.

[43]  Jose L. Salmeron,et al.  A Review of Fuzzy Cognitive Maps Research During the Last Decade , 2013, IEEE Transactions on Fuzzy Systems.

[44]  D. Angelaki,et al.  A computational perspective on autism , 2015, Proceedings of the National Academy of Sciences.

[45]  José Aguilar,et al.  Different dynamic causal relationship approaches for cognitive maps , 2013, Appl. Soft Comput..

[46]  C. Ecker.  The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting , 2017, Autism : the international journal of research and practice.

[47]  José Aguilar,et al.  A Fuzzy Cognitive Map Based on the Random Neural Model , 2001, IEA/AIE.

[48]  C. Corsello,et al.  Diagnostic Instruments in Autistic Spectrum Disorders , 2005 .

[49]  Vigneshwaran Subbaraju,et al.  Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network , 2015, Expert Syst. Appl..