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. 2021 Mar 30:2021:1628959.
doi: 10.1155/2021/1628959. eCollection 2021.

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

Evandro Andrade  1 , Samuel Portela  1 , Plácido Rogério Pinheiro  1 , Luciano Comin Nunes  1 , Marum Simão Filho  1 , Wagner Silva Costa  1 , Mirian Caliope Dantas Pinheiro  1
Affiliations

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

Evandro Andrade et al. Comput Math Methods Med. .

Abstract

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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Conflict of interest statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
Neurodevelopmental Disorders, according to DSM-5. Source: Nunes et al. [3].
Figure 2
Figure 2
The graph with the processing structure of the proposed hybrid model. Source: formatted by the author.
Figure 3
Figure 3
The Random Forest algorithm. Source: Scornet et al. [22].
Figure 4
Figure 4
Verbal Decision Analysis methodologies. Source: Nunes et al. [3].
Figure 5
Figure 5
Procedure for applying the ZAPROS-IIIi method. Source: Pinheiro et al. [30].
Figure 6
Figure 6
Configuration for using Random Forest. Source: formatted by the author.
Figure 7
Figure 7
The sampling of the main alternatives considered in the evaluation of the resulting tree. Source: formatted by the author.
Figure 8
Figure 8
Order of preference among the main characteristics of ASD. Source: formatted by the author.
See this image and copyright information in PMC

References

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    1. Nunes L. C., Pinheiro P. R., Pinheiro M. C. D., et al. A hybrid model to guide the consultation of children with autism spectrum disorder. In: Visvizi A., Lytras M. D., editors. Springer Proceedings in Complexity. 1. Springer International Publishing; 2019. pp. 419–431.
    1. APA–American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM –5) 2020. https://www.psychiatry.org/psychiatrists/practice/dsm.
    1. Martone M. C. C., Santos-Carvalho L. H. Z. A review of articles published in the Journal of Applied Behavior Analysis (JABA) on verbal behavior and autism between 2008 and 2012. 2012.