Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques
2024
Improving autism spectrum disorder diagnosis using machine learning techniques
Editorial
Author Information
Author(s): Elbattah Mahmoud, Ali Sadek Ibrahim Osman, Dequen Gilles
Primary Institution: Laboratoire MIS, Université de Picardie Jules Verne, Amiens, France
Conclusion
Machine learning techniques have the potential to enhance the accuracy and efficiency of autism spectrum disorder diagnosis.
Supporting Evidence
- Machine Learning has shown high accuracy in classifying autism using eye-tracking data.
- Unsupervised learning techniques have provided insights into the variability of autism presentations.
- Multimodal approaches integrating various data types have improved diagnostic precision.
Takeaway
This study talks about using computers to help doctors figure out if someone has autism, which can make the process faster and more accurate.
Limitations
Challenges include standardization of methodologies and reproducibility of results.
Digital Object Identifier (DOI)
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