Multi-Modal Explainable Machine Learning for Automated Detection of Autistic Spectrum Disorder - Archive ouverte HAL
Reports (Research Report) Year : 2024

Multi-Modal Explainable Machine Learning for Automated Detection of Autistic Spectrum Disorder

Meryem Ben Yahia
  • Function : Author
Moncef Garouani
Julien Aligon

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by diverse symptoms affecting social interaction, communication, and behavior. Diagnosing ASD is challenging due to variability among individuals and the lack of clear biomarkers. Although artificial intelligence (AI) has enhanced diagnostic accuracy, the "black-box" nature of many models limits their clinical use. Addressing current limitations, this paper introduces a multimodal ASD detection framework using deep neural networks (DNN) with explainable AI (xAI) to enhance model transparency. Our proposed approach achieves a mean 5-fold accuracy of 99.66% for the fMRI-based model and 99.83% for the multimodal-based model, which leverages both fMRI and phenotypic data, surpassing existing methods while offering better interpretability.

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Dates and versions

hal-04725536 , version 1 (08-10-2024)

Identifiers

  • HAL Id : hal-04725536 , version 1

Cite

Meryem Ben Yahia, Moncef Garouani, Julien Aligon. Multi-Modal Explainable Machine Learning for Automated Detection of Autistic Spectrum Disorder. Institut de Recherche en Informatique de Toulouse (IRIT). 2024. ⟨hal-04725536⟩
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