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Related Experiment Videos

Predicting Autism Spectrum Disorder in Children Using Glowworm Optimization With Extreme Learning Machine Networks.

Vijay Govindarajan1, Ashit Kumar Dutta2,3, Zaffar Ahmed Shaikh4,5

  • 1Distribution and Supply Technology, Expedia Group, Seattle, Washington, USA.

Brain and Behavior
|February 17, 2026
PubMed
Summary

Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

1.3K
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
1.3K

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This summary is machine-generated.

This study introduces an optimized model for early autism spectrum disorder (ASD) detection, offering a fast and accurate solution. The system aims to improve accessibility and intervention for children

Area of Science:

  • Pediatric Healthcare
  • Machine Learning Applications
  • Developmental Disorders

Background:

  • Early prediction of autism spectrum disorder (ASD) is crucial for timely intervention, improving developmental outcomes.
  • Current ASD detection methods are often time-consuming, subjective, and lack accessibility, particularly in rural areas.
  • Developing scalable, objective, and rapid ASD detection systems is essential to overcome healthcare challenges.

Purpose of the Study:

  • To enhance the efficiency and accuracy of autism spectrum disorder (ASD) prediction.
  • To address limitations in current ASD detection processes, including time constraints and accessibility issues.
  • To provide a reliable and scalable solution for early ASD identification in pediatric healthcare.

Main Methods:

Keywords:
autism spectrum disorderglowworm optimization with extreme learning machine networkshyperparameterspediatric healthcare

Related Experiment Videos

  • Integration of the Glowworm Optimization with Extreme Learning Machine Networks (GO-ELMN) model for ASD prediction.
  • Utilizing behavioral, demographic, and medical features from children's ASD screening data.
  • Optimizing network hyperparameters with the glowworm optimization algorithm to handle limited and imbalanced data.

    • Main Results:

    • The GO-ELMN model demonstrated high accuracy in ASD prediction.
    • The system achieved a fast convergence speed, indicating computational efficiency.
    • Experimental results validated the system's effectiveness in identifying children's behavior patterns.

    Conclusions:

    • The developed ASD detection model offers an interpretable, fast, and reliable solution.
    • The system is suitable for effective utilization within the pediatric healthcare domain.
    • This approach addresses key challenges in early autism spectrum disorder identification.