On the voice signal analysis for the diagnosis of Parkinson's disease
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1305Keywords:
Diagnosis, Machine Learning, Parkinson DiseaseAbstract
Objetivo: Este estudo investiga se o possível viés na sobreamostragem via janelamento de dados de marcha em indivíduos com Doença de Parkinson (DP) também ocorre em sinais vocais. Um estudo anterior levantou a hipótese de que amostras distintas de um mesmo indivíduo não devem ser tratadas independentemente, dado o risco de enviesamento dos modelos. Método: Usamos sinais de voz de 24 indivíduos com DP e 8 saudáveis, e os algoritmos K-Nearest Neighbors (KNN), Support Vector Machine (SVM) e Random Forest (RF). A validação cruzada foi feita com Leave-one-out (LOOCV), adaptada para cenários com e sem viés nos dados de treinamento. Resultados: Modelos avaliados sem considerar o viés apresentaram performances inflacionadas, enquanto a abordagem rigorosa mostrou resultados mais modestos. Conclusão: Amostras do mesmo indivíduo em treinamento e teste podem inflar a performance dos modelos. A correta aplicação da sobreamostragem é crucial para desenvolver modelos confiáveis para o diagnóstico de DP.
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