Explicabilidade em Modelos Preditivos de Machine Learning no Câncer de Mama
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1090Palavras-chave:
Câncer de Mama, Aprendizado de Máquina, Inteligência ArtificialResumo
Objetivo: A Inteligência Artificial se mostra promissora como apoio à decisão no câncer de mama, porém, a interpretabilidade dos algoritmos como os de caixa preta pode contribuir na adoção na prática clínica. Esse estudo apresenta a explicabilidade em Modelos Preditivos de Aprendizado de Máquina no Câncer de Mama. Métodos: Avaliou-se duas abordagens distintas de Aprendizado de Máquina, Multilayer Perceptron (MLP) e Extreme Gradient Boosting (XGBoost), considerando amostra de 164 mulheres submetidas a Core Biópsia entre 2014 e 2015. Utilizou-se o Shapley Additive Explanation para a explicabilidade dos modelos. Resultados: Os modelos preditivos apresentaram, ambos, acurácia de 98,0% (IC95%:94,2%-100,0%) e o BI-RADS® 5 no ultrassom foi considerado como o atributo mais importante. Conclusão: Os modelos mostraram alta capacidade preditiva para o câncer de mama; no MLP o BI-RADS® 3 e 5 do ultrassom foram os atributos mais importantes, e no XGB, além do ultrassom, destacaram-se a idade e o nódulo palpável.
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