Predicción del deterioro cognitivo leve: integración de variables cognitivas y motoras
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1258Palabras clave:
Pronóstico, Aprendizaje Automático, Deterioro Cognitivo LeveResumen
Objetivo: El Deterioro Cognitivo Leve (DCL) representa un estadio intermedio entre el envejecimiento normal y la demencia, que requiere una detección precoz para evitar su progresión. Este estudio pretende desarrollar un modelo de clasificación de aprendizaje automático para predecir con precisión el pronóstico de los individuos con DCL, diferenciándolos de los sanos. Método: El método integra variables motoras y cognitivas, así como información autoinformada. Se aplicaron los algoritmos SVM, KNN y XGBoost. La mejor predicción se evaluó mediante el método del valor de Shapley para determinar la importancia de cada variable. Resultados: El SVM produjo los mejores resultados, alcanzando 88% de sensibilidad y revelando que las variables del dominio motor y de los dominios cognitivo y motor son altamente relevantes para la clasificación. Conclusión: Además de ser más accesible, el método desarrollado presentó alta sensibilidad en la clasificación de DCL basada en la integración de variables cognitivas y motoras.
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