Aprendizaje automático en el diagnóstico médico. Un caso de estudio en la identificación del Trastorno del Espectro Autista a partir del comportamiento ocular

  1. Chávez-Trujillo, Roberto 1
  2. Aguilar, Rosa M. 1
  3. González-Mora, José Luis 1
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2024

Volumen: 21

Número: 3

Páginas: 205-217

Tipo: Artículo

DOI: 10.4995/RIAI.2024.20484 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumen

A pesar de los avances recientes, el diagnóstico del autismo sigue siendo un desafío complejo debido a la necesidad de recursos médicos especializados, tiempo y materiales. Esto a menudo resulta en diagnósticos tardíos, incluso en la edad adulta, dificultando las intervenciones efectivas. Por otro lado, el campo de la inteligencia artificial y el aprendizaje automático ha experimentado un notable progreso. Estas técnicas han abierto nuevas oportunidades entre otras muchas áreas, en el diagnóstico médico, incluyendo el Trastorno del Espectro Autista (TEA). El objetivo principal de este artículo es ofrecer una visión general de la aplicabilidad de las técnicas de aprendizaje automático en el diagnóstico médico, a través de un caso de uso específico en el TEA. Se ha desarrollado un modelo de clasificación basado en el algoritmo XGBoost, que logra una sensibilidad del 82 % y una especificidad del 74 % al clasificar muestras individuales. Además, al combinar este modelo con un algoritmo de votación por mayoría, se obtienen unos muy destacados resultados de clasificación en el conjunto de pruebas.

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Fuente de los datos: Dialnet