Aplicação de Árvore de Decisão para Auxı́lio ao Diagnóstico do Transtorno do Espectro Autista
Abstract
Machine Learning algorithms are being applied successfully to many areas of knowledge, in the health field, those algorithms allows professionals to be assisted to diagnose diseases and disorders more anticipatedly and more accurately, contributing to the effecttive treatment of their patients. In this context, the Decision Tree algorithm used in this paper proposes to build a model capable of simplify a complex set of decisions and produce a strategy based on an public and international dataset about the Autism Spectrum Disorder (ASD), allowing to develop a complement in it diagnosis and treatment.
References
APA, A. P. A. (2014). DSM-5 - Manual Diagnóstico e Estatı́stico de Transtornos Mentais. Artmed.
Ishwaran, H. (2015). The effect of splitting on random forests. Machine Learning, 99(1):75–118.
Liu, W., Li, M., and Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8):888–898.
One, A. and Two, A. (2019). Análise de desempenho com redes neurais artificiais, arquiteturas MLP e RBF para um problema de classificação de crianças com autismo. iSys-Brazilian Journal of Information Systems, 12(1).
Rezende, S. O., Monard, M. C., and Carvalho, A. C. P. d. L. (1999). Sistemas inteligentes para engenharia: pesquisa e desenvolvimento. Anais III Workshop de Sistemas Inteligentes para Engenharia.
Safavian, S. R. and Landgrebe, D. (1991). A survey of decision tree classifier methodo-logy. IEEE transactions on systems, man, and cybernetics, 21(3):660–674.
Speaks, A. (2019). Autism speaks. https://www.autismspeaks.org/. Acesso em: 05/04/2019.
Thabtah, F. F. (2017). Autistic spectrum disorder screening data for children data set. https://archive.ics.uci.edu/ml/datasets/Autistic+Spectrum+Disorder+Screening+Data+for+Children++. Acesso em: 08/07/2019.
Ishwaran, H. (2015). The effect of splitting on random forests. Machine Learning, 99(1):75–118.
Liu, W., Li, M., and Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8):888–898.
One, A. and Two, A. (2019). Análise de desempenho com redes neurais artificiais, arquiteturas MLP e RBF para um problema de classificação de crianças com autismo. iSys-Brazilian Journal of Information Systems, 12(1).
Rezende, S. O., Monard, M. C., and Carvalho, A. C. P. d. L. (1999). Sistemas inteligentes para engenharia: pesquisa e desenvolvimento. Anais III Workshop de Sistemas Inteligentes para Engenharia.
Safavian, S. R. and Landgrebe, D. (1991). A survey of decision tree classifier methodo-logy. IEEE transactions on systems, man, and cybernetics, 21(3):660–674.
Speaks, A. (2019). Autism speaks. https://www.autismspeaks.org/. Acesso em: 05/04/2019.
Thabtah, F. F. (2017). Autistic spectrum disorder screening data for children data set. https://archive.ics.uci.edu/ml/datasets/Autistic+Spectrum+Disorder+Screening+Data+for+Children++. Acesso em: 08/07/2019.
Published
2019-12-26
How to Cite
FROTA, Matheus; VILELA, Manoel ; HERICLES, Samuel ; AGUIAR, Gerônimo ; RENOIR, Pedro; NUNES, Rayon ; GOMES, Denilson ; CAVALCANTE, Iális .
Aplicação de Árvore de Decisão para Auxı́lio ao Diagnóstico do Transtorno do Espectro Autista. In: REGIONAL SCHOOL OF APPLIED COMPUTING FOR HEALTH (ERCAS), 7. , 2019, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 205-210.
