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Algoritmo Genético de Una Clase para el Análisis de Autenticación de Datos Espectroquímicos

José R de Morais Filho1, Camilo de L M de Morais2, Anne B F Câmara1

  • 1Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN 5072-970, Brazil.

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Una nueva estrategia de selección de variables, el algoritmo genético de una clase (OGA), mejoró la clasificación para la detección de COVID-19, endometriosis y dengue. Este enfoque quimiométrico mejora el diagnóstico de enfermedades y el descubrimiento de biomarcadores.

Palabras clave:
Algoritmo Genético de Una ClaseQuimiometríaSelección de VariablesClasificación de EnfermedadesDescubrimiento de BiomarcadoresCOVID-19EndometriosisDengueAprendizaje AutomáticoDiagnóstico Clínico

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Área de la Ciencia:

  • Quimiometría
  • Aprendizaje Automático
  • Diagnóstico Clínico

Sus antecedentes:

  • Los modelos de clasificador de una clase (OCC) son esenciales para la modelización y el control de clases objetivo.
  • Los modelos OCC existentes como DD-SIMCA y OCPLS requieren una selección robusta de variables para un rendimiento óptimo.

Objetivo del estudio:

  • Integrar un novedoso algoritmo genético de una clase (OGA) con los modelos DD-SIMCA y OCPLS.
  • Evaluar el rendimiento de clasificación mejorado en aplicaciones clínicas que incluyen COVID-19, endometriosis y dengue.

Principales métodos:

  • Implementación de DD-SIMCA con alfa = 0.05 y OCPLS con M-regresión robusta parcial (PRM).
  • Asociación de estos modelos OCC con el algoritmo genético de una clase (OGA) para la selección de variables.
  • Aplicación y comparación de los modelos OGA-DD-SIMCA y OGA-PRM-OCPLS en conjuntos de datos clínicos.

Principales resultados:

  • El OGA mejoró significativamente el rendimiento de clasificación en las tres aplicaciones clínicas.
  • OGA-PRM-OCPLS logró una sensibilidad del 100% para COVID-19 y endometriosis.
  • OGA-DD-SIMCA demostró un rendimiento superior para la clasificación del dengue con una sensibilidad del 100%.

Conclusiones:

  • El OGA mejora la utilidad del modelo OCC para la clasificación de enfermedades y la identificación de biomarcadores.
  • Este enfoque quimiométrico ofrece potencial para desarrollar métodos de cribado de diagnóstico rápidos, de bajo costo y mínimamente invasivos.