Robustness and Explainability of Machine Learning Algorithms for Autism Spectrum Disorder Screening
Resumo
Este trabalho avalia robustez e explicabilidade de algoritmos de aprendizado de máquina na triagem do Transtorno do Espectro Autista (TEA) com dados do AQ-10-CHILD. Foram analisados cenários com ruído artificial para simular erros de preenchimento, com interpretação das predições por valores SHAP. Os resultados indicam que Regressão Logística, SVM e Perceptron Multicamadas tendem a uma lógica aditiva. Florestas Aleatórias, KNN e Naive Bayes apresentaram maior estabilidade e melhor capacidade de capturar interações entre indicadores. Argumentamos assim que a escolha do modelo de predição deve considerar, além da acurácia, também a resiliência destes ao ruído e a sua interpretabilidade clínica, para que este seja adequado a cenários reais.
Referências
Allison, C., Auyeung, B., and Baron-Cohen, S. (2012a). Toward brief “red flags” for autism screening: The short autism spectrum quotient and the short quantitative checklist in 1,000 cases and 3,000 controls. Journal of the American Academy of Child & Adolescent Psychiatry, 51(2):202–212.
Allison, C., Baron-Cohen, S., Wheelwright, S., Sastry, B., Green, J., Argyle, E., and Brayne, C. (2012b). Short report: a short strategy for identifying children at high risk of autism spectrum conditions in primary care. Molecular Autism, 3(1):1–7.
APA, A. P. A. (2013). Diagnostic and statistical manual of mental disorders. The American Psychiatric Association.
Artoni, A. A., Barbosa, C., and Morandini, M. (2022). Autism spectrum disorder diagnosis assistance using machine learning. Revista de Informática Teórica e Aplicada, 29(3):36–53.
Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., and Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of autism and developmental disorders, 31:5–17.
Fitzgerald, M. (2017). The clinical gestalts of autism: over 40 years of clinical experience with autism. In Autism-paradigms, recent research and clinical applications. IntechOpen.
Garg, A., Parashar, A., Barman, D., Jain, S., Singhal, D., Masud, M., and Abouhawwash, M. (2022). Autism spectrum disorder prediction by an explainable deep learning approach. Computers, Materials & Continua, 71(1):1459–1471.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825):357–362.
Hossain, M. D., Kabir, M. A., Anwar, A., and Islam, M. Z. (2021). Detecting autism spectrum disorder using machine learning techniques: An experimental analysis on toddler, child, adolescent and adult datasets. Health Information Science and Systems, 9:1–13.
Lino, S., Cruz, L., and Guerra, P. T. (2024). Análise da robustez de algoritmos de aprendizado de máquina em dados do transtorno do espectro autista. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 53–65, Porto Alegre, RS, Brasil. SBC.
Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., Pickles, A., and Rutter, M. (2000). The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of autism and developmental disorders, 30:205–223.
Lord, C., Rutter, M., and Le Couteur, A. (1994). Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of autism and developmental disorders, 24(5):659–685.
Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Thabtah, F., Abdelhamid, N., and Peebles, D. (2019). A machine learning autism classification based on logistic regression analysis. Health information science and systems, 7:1–11.
Thabtah, F., Kamalov, F., and Rajab, K. (2018). A new computational intelligence approach to detect autistic features for autism screening. International journal of medical informatics, 117:112–124.
Wes McKinney (2010). Data Structures for Statistical Computing in Python. In Stéfan van der Walt and Jarrod Millman, editors, Proceedings of the 9th Python in Science Conference, pages 56 – 61.
Allison, C., Baron-Cohen, S., Wheelwright, S., Sastry, B., Green, J., Argyle, E., and Brayne, C. (2012b). Short report: a short strategy for identifying children at high risk of autism spectrum conditions in primary care. Molecular Autism, 3(1):1–7.
APA, A. P. A. (2013). Diagnostic and statistical manual of mental disorders. The American Psychiatric Association.
Artoni, A. A., Barbosa, C., and Morandini, M. (2022). Autism spectrum disorder diagnosis assistance using machine learning. Revista de Informática Teórica e Aplicada, 29(3):36–53.
Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., and Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of autism and developmental disorders, 31:5–17.
Fitzgerald, M. (2017). The clinical gestalts of autism: over 40 years of clinical experience with autism. In Autism-paradigms, recent research and clinical applications. IntechOpen.
Garg, A., Parashar, A., Barman, D., Jain, S., Singhal, D., Masud, M., and Abouhawwash, M. (2022). Autism spectrum disorder prediction by an explainable deep learning approach. Computers, Materials & Continua, 71(1):1459–1471.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825):357–362.
Hossain, M. D., Kabir, M. A., Anwar, A., and Islam, M. Z. (2021). Detecting autism spectrum disorder using machine learning techniques: An experimental analysis on toddler, child, adolescent and adult datasets. Health Information Science and Systems, 9:1–13.
Lino, S., Cruz, L., and Guerra, P. T. (2024). Análise da robustez de algoritmos de aprendizado de máquina em dados do transtorno do espectro autista. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 53–65, Porto Alegre, RS, Brasil. SBC.
Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., Pickles, A., and Rutter, M. (2000). The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of autism and developmental disorders, 30:205–223.
Lord, C., Rutter, M., and Le Couteur, A. (1994). Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of autism and developmental disorders, 24(5):659–685.
Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Thabtah, F., Abdelhamid, N., and Peebles, D. (2019). A machine learning autism classification based on logistic regression analysis. Health information science and systems, 7:1–11.
Thabtah, F., Kamalov, F., and Rajab, K. (2018). A new computational intelligence approach to detect autistic features for autism screening. International journal of medical informatics, 117:112–124.
Wes McKinney (2010). Data Structures for Statistical Computing in Python. In Stéfan van der Walt and Jarrod Millman, editors, Proceedings of the 9th Python in Science Conference, pages 56 – 61.
Publicado
19/10/2026
Como Citar
LINO, Saulo B. F.; GUERRA, Paulo T..
Robustness and Explainability of Machine Learning Algorithms for Autism Spectrum Disorder Screening. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 2703-2714.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.30169.
