Uma prova de conceito para o diagnóstico diferencial do transtorno do espectro autista usando aprendizado de máquina
DOI:
https://doi.org/10.51359/1679-1827.2024.263456Palavras-chave:
aprendizagem de máquina, autismo, diagnóstico, eletroencefalogramaResumo
Objetivo: O Transtorno do Espectro do Autismo (TEA) é um transtorno do neurodesenvolvimento caracterizado por déficits na comunicação social e na interação social. Atualmente, o diagnóstico do TEA é clínico. Neste contexto, este trabalho busca validar uma nova proposta de ferramenta para diagnóstico diferencial do TEA, de suporte à decisão do especialista, baseada na utilização de aprendizagem de máquina e sinais de EEG.
Método/abordagem: Utilizou-se o dataset 1 da base de dados Sheffield após processamento. Foram extraídos e selecionados atributos para avaliação utilizando 2 métodos: otimização por enxame de partículas e busca evolucionária. Os dois conjuntos foram divididos em treino (80%) e teste (20%), aplicou-se validação cruzada com 10 folds e, em seguida, avaliou-se 12 modelos de classificação distintos. Os experimentos foram repetidos 30 vezes.
Contribuições teóricas/práticas/sociais: O modelo com melhores resultados foi o SVM Rbf 0.5, com bons valores de acurácia (99,31%), índice Kappa (0,986), sensibilidade (0,994), especificidade (0,992) e área ROC (0,993).
Originalidade/relevância: Os resultados sugerem que a aprendizagem de máquina é uma ferramenta eficaz no diagnóstico do TEA.
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