scholarly journals A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Evandro Andrade ◽  
Samuel Portela ◽  
Plácido Rogério Pinheiro ◽  
Luciano Comin Nunes ◽  
Marum Simão Filho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Decision Analysis ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
World Health ◽  
Diagnostic Process ◽  
Icd 10 ◽  
Verbal Decision Analysis

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.

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.

scholarly journals Accuracy of machine learning algorithms for the diagnosis of autism spectrum disorder based on cerebral sMRI, rs-fMRI, and EEG: protocols for three systematic reviews and meta-analyses

2021 ◽  
Author(s):  
Amir Valizadeh ◽  
Mana Moassefi ◽  
Amin Nakhostin-Ansari ◽  
Iman Menbari Oskoie ◽  
Soheil Heidari Some'eh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Diagnostic Accuracy ◽  
Learning Algorithms ◽  
Grey Literature ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Cross Sectional ◽  
Medicine Research

Objective: To determine the diagnostic accuracy of the applied machine learning algorithms for the diagnosis of autism spectrum disorder (ASD) based on structural magnetic resonance imaging (sMRI), resting-state functional MRI (rs-fMRI), and electroencephalography (EEG). Methods: We will include cross-sectional studies (both single-gates and two-gates) that have evaluated the diagnostic accuracy of machine learning algorithms on the sMRI data of ASD patients regardless of age, sex, and ethnicity. On the 22nd of May 2021, we searched Embase, MEDLINE, APA PsycINFO, IEEE Xplore, Scopus, and Web of Science for eligible studies. We also searched grey literature within various sources. We will use an adapted version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Data will be synthesized using the relatively new Split Component Synthesis (SCS) method. We plan to assess heterogeneity using the I2 statistics and assess publication bias using trim and fill tests combined with ln DOR. Certainty of evidence will be assessed using the GRADE approach for diagnostic studies. Funding: These studies are funded by Sports Medicine Research Center, Tehran, Iran. Registration: PROSPERO submission IDs: 262575, 262825, and 262831.

scholarly journals Optimization of Machine Learning Techniques on Autism Spectrum Disorder with Swarm Intelligence Based Feature Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 6248-6251
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Swarm Intelligence ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Dsm V

This paper is a study on the various machine learning algorithms in order to perform ASD (Autism spectrum Disorder) as per the DSM-V standards. ASD occurs more frequently among children and in order to diagnose this with better accuracy, the study on binary firefly algorithm, a swarm intelligence based wrapper feature selection algorithm is used to obtain best results with optimum feature subsets. This paper will provide overall result after applying it to all types of machine learning models on supervised learning.

scholarly journals Machine Learning based Autism Grading for Clinical Decision Making

2019 ◽  
Vol 8 (4) ◽  
pp. 7443-7446
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Autism Spectrum Disorder ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Clinical Decision ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Diagnostic Process ◽  
Manhattan Distance

Autism spectrum disorder is a pervasive developmental disorder that affects the behavioral and communication function of the children. It shows poor performance in communication, social and cognitive abilities, which are generally characterized by developmental delays and abnormal activities in their regular work. Early intervention can reduce the autism spectrum disorders. Machine learning techniques are used to detect autistic features in childhood. The prediction models are implemented as classification problem in which model is constructed by using real-time autism dataset. The proposed work is use Backpropagation and learning vector quantization with different distance measures like Euclidean Distance, Manhattan Distance, and City Block Distance to predict whether a child has autism spectrum disorder and also defines the grade of the autism. So that it can be supported for the clinical decision making. It enables automated clinical autism spectrum disorder diagnostic process using machine learning models.

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