Previsão do comprometimento cognitivo leve: integrando variáveis cognitivas e motoras
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1258Palavras-chave:
Prognóstico, Aprendizado de Máquina, Comprometimento Cognitivo LeveResumo
Objetivo: O Comprometimento Cognitivo Leve (CCL) representa uma fase intermediária entre o envelhecimento normal e a demência, exigindo uma detecção precoce para impedir a sua progressão. Este estudo tem como objetivo desenvolver um modelo de classificação de aprendizado de máquina para prever com precisão o prognóstico de indivíduos com CCL, diferenciando-os dos saudáveis. Método: O método integra variáveis motoras e cognitivas, além de informações autorrelatadas. Foram aplicados os algoritmos SVM, KNN e XGBoost. A melhor previsão foi avaliada pelo método Shapley Value para determinação da importância de cada variável. Resultados: O SVM apresentou melhor resultado, alcançando 88% de sensibilidade e revelando que as variáveis do domínio motor e dos domínios cognitivo e motor são altamente relevantes para a classificação. Conclusão: O método desenvolvido, além de ser mais acessível, apresentou alta sensibilidade na classificação do CCL a partir da integração de variáveis cognitivas e motoras.
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