Comparação de métodos preditivos de Machine Learning para diagnosticar os níveis do Transtorno de Déficit de Atenção/Hiperatividade usando imagens SPECT
DOI:
https://doi.org/10.33448/rsd-v11i8.31258Palavras-chave:
Diagnóstico assistido de TDAH; Diagnóstico auxiliado por computador; Aprendizado de máquina; Medicina nuclear; SPECT.Resumo
O TDAH (transtorno de déficit de atenção e hiperatividade) é um transtorno do neurodesenvolvimento caracterizado por níveis prejudiciais de desatenção, desorganização e/ou hiperatividade-impulsividade. Na infância, esses sintomas muitas vezes se sobrepõem aos de outros transtornos e tendem a persistir na vida adulta, interferindo nos relacionamentos e na vida acadêmica e profissional. O diagnóstico, tradicionalmente feito pela avaliação do paciente, ou seja, testando e ouvindo familiares e professores, já tem sido auxiliado pela neuroimagem. No entanto, a análise visual de tais imagens para fazer um diagnóstico psiquiátrico é uma tarefa complexa e às vezes demorada. Por esse motivo, têm evoluído cada vez mais ferramentas de diagnóstico auxiliadas por computador que, quando combinadas com técnicas de aprendizado de máquina (ML), podem acelerar, facilitar e maximizar a precisão dos diagnósticos. No entanto, pesquisas avaliando modelos de ML para classificar o TDAH considerando a gravidade usando imagens do cérebro SPECT (Tomografia Computadorizada por Emissão de Fóton Único) ainda são muito escassas. Por esse motivo, este artigo tem como objetivo avaliar o desempenho dos métodos de ML: k-NN (k-Nearest Neighbors), Naive Bayes, Decision Tree, MLP (Multilayer Perceptron) e SVM (Support Vector Machine) na classificação do TDAH. O principal objetivo desta análise é verificar se os sujeitos têm ou não o transtorno e classificar a gravidade daqueles que o têm usando imagens SPECT. Um banco de dados foi criado a partir de imagens SPECT e relatórios de diagnóstico. Após o pré-processamento desses dados, os melhores hiperparâmetros para os métodos de ML foram pesquisados, treinados/testados e por fim comparados estatisticamente. Os melhores resultados foram obtidos com SVM e k-NN, com 98% de acurácia. Embora o diagnóstico de TDAH por neuroimagem ainda não seja um procedimento clínico padrão, argumentamos que este estudo pode contribuir para a pesquisa do diagnóstico de TDAH e apoiar métodos para o desenvolvimento de sistemas CAD (computer-aided diagnosis).
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