Análise da Robustez de Algoritmos de Aprendizado de Máquina em Dados do Transtorno do Espectro Autista
Resumo
O Transtorno do Espectro Autista (TEA) é uma condição neurológica que afeta a comunicação, interação social, comportamento e aprendizado. Métodos de triagem como AQ e Q-CHAT foram desenvolvidos para agilizar a identificação de sinais autistas. O presente trabalho analisa o desempenho de algoritmos de aprendizado de máquina na triagem do TEA, tais como SVM, MLP, Regressão Logística, Naive Bayes, Floresta Aleatória e KNN, e a robustez destes modelos diante de possíveis erros nos dados. Os algoritmos são avaliados em conjuntos de dados com amostras baseadas em características pessoais e questões simplificadas dos instrumentos AQ e Q-CHAT. Os experimentos apontam um bom desempenho obtido pelos métodos SVM, MLP e Regressão Logística, porém com significativa redução da acurácia em cenários com erros.
Palavras-chave:
Aprendizado de Máquina, Algoritmos de Classificação, Qualidade de Dados e Informação
Referências
Allison, C., Auyeung, B., and Baron-Cohen, S. (2012). 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., Charman, T., Richler, J., Pasco, G., and Brayne, C. (2008). The q-chat (quantitative checklist for autism in toddlers): a normally distributed quantitative measure of autistic traits at 18–24 months of age: preliminary report. Journal of autism and developmental disorders, 38:1414–1425.
APA, A. P. A. (2013). Diagnostic and Statistical Manual of Mental Disorders. 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, malesand females, scientists and mathematicians. Journal of autism and developmental disorders, 31:5–17.
Ferreira, R. d. S. (2010). Autism testing: Uma ferramenta móvel no auxílio ao pré-diagnóstico do autismo. In Anais do XXII Conferência Internacional sobre Informática na Educação. Fortaleza, Ceará-Brasil: Nuevas Ideas en Informática Educativa, volume 13, pages 178–187.
Fitzgerald, M. (2017). The clinical gestalts of autism: Over 40 years of clinical experience with autism. In Fitzgerald, M. and Yip, J., editors, Autism, chapter 2. 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.
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.
Kleinman, J. M., Robins, D. L., Ventola, P. E., Pandey, J., Boorstein, H. C., Esser, E. L., Wilson, L. B., Rosenthal, M. A., Sutera, S., Verbalis, A. D., Barton, M., Hodgson, S., Green, J., Dumont-Mathieu, T., Volkmar, F., Chawarska, K., Klin, A., and Fein, D. (2008). The modified checklist for autism in toddlers: A follow-up study investigating the early detection of autism spectrum disorders. Journal of Autism and Developmental Disorders, 38(5):827–839.
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. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). ”why should i trust you?”explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144.
Thabtah, F. (2017). ASDTests: A mobile app for ASD screening. Disponível em: [link]. Acesso em: 10 de maio de 2024.
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.
Thabtah, F. and Peebles, D. (2020). A new machine learning model based on induction of rules for autism detection. Health informatics journal, 26(1):264–286.
Allison, C., Baron-Cohen, S., Wheelwright, S., Charman, T., Richler, J., Pasco, G., and Brayne, C. (2008). The q-chat (quantitative checklist for autism in toddlers): a normally distributed quantitative measure of autistic traits at 18–24 months of age: preliminary report. Journal of autism and developmental disorders, 38:1414–1425.
APA, A. P. A. (2013). Diagnostic and Statistical Manual of Mental Disorders. 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, malesand females, scientists and mathematicians. Journal of autism and developmental disorders, 31:5–17.
Ferreira, R. d. S. (2010). Autism testing: Uma ferramenta móvel no auxílio ao pré-diagnóstico do autismo. In Anais do XXII Conferência Internacional sobre Informática na Educação. Fortaleza, Ceará-Brasil: Nuevas Ideas en Informática Educativa, volume 13, pages 178–187.
Fitzgerald, M. (2017). The clinical gestalts of autism: Over 40 years of clinical experience with autism. In Fitzgerald, M. and Yip, J., editors, Autism, chapter 2. 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.
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.
Kleinman, J. M., Robins, D. L., Ventola, P. E., Pandey, J., Boorstein, H. C., Esser, E. L., Wilson, L. B., Rosenthal, M. A., Sutera, S., Verbalis, A. D., Barton, M., Hodgson, S., Green, J., Dumont-Mathieu, T., Volkmar, F., Chawarska, K., Klin, A., and Fein, D. (2008). The modified checklist for autism in toddlers: A follow-up study investigating the early detection of autism spectrum disorders. Journal of Autism and Developmental Disorders, 38(5):827–839.
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. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). ”why should i trust you?”explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144.
Thabtah, F. (2017). ASDTests: A mobile app for ASD screening. Disponível em: [link]. Acesso em: 10 de maio de 2024.
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.
Thabtah, F. and Peebles, D. (2020). A new machine learning model based on induction of rules for autism detection. Health informatics journal, 26(1):264–286.
Publicado
14/10/2024
Como Citar
LINO, Saulo B. F.; CRUZ, Lívia A.; GUERRA, Paulo T..
Análise da Robustez de Algoritmos de Aprendizado de Máquina em Dados do Transtorno do Espectro Autista. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 53-65.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240567.
