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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

98
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.
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Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder.

Mohemmed Sha1, Abdullah Alqahtani1, Shtwai Alsubai2

  • 1Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.

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Summary
This summary is machine-generated.

A novel modified artificial neural network (ANN) model accurately identifies autism spectrum disorder (ASD) in children and adolescents. This AI-driven approach enhances early diagnosis, improving quality of life for individuals with ASD.

Keywords:
artificial neural networksautism screeningautism spectrum disorderbat algorithmdecision treek-nearest neighbours

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Area of Science:

  • Neuroscience and Developmental Disorders
  • Artificial Intelligence in Healthcare
  • Machine Learning for Diagnostics

Background:

  • Autism spectrum disorder (ASD) is a complex neurodevelopmental condition affecting social interaction and communication.
  • Early diagnosis of ASD is crucial for improving patient outcomes and quality of life.
  • Traditional ASD screening methods are time-consuming, expensive, and face limitations with large datasets, accuracy, and speed.

Purpose of the Study:

  • To develop an advanced and efficient system for identifying autism spectrum disorder (ASD) in children and adolescents.
  • To overcome the limitations of conventional ASD detection methods using artificial intelligence.
  • To enhance the accuracy and speed of ASD classification through optimized algorithms.

Main Methods:

  • Utilized a modified bat algorithm (MBA) for optimization, integrated with artificial neural networks (ANN), modified ANN, decision tree (DT), and k-nearest neighbours (KNN) for ASD classification.
  • Employed the Q-chat-10 dataset, encompassing data from toddlers to adults, for model training and validation.
  • Applied dataset evaluation mechanisms like Chi-Squared Statistic and p-value to assess data-model relevance and employed performance metrics for efficiency analysis.

Main Results:

  • The modified artificial neural network (ANN) classifier model achieved a perfect accuracy of 1.00.
  • The proposed system demonstrated superior performance compared to existing state-of-the-art methods in ASD detection.
  • The modified bat algorithm (MBA) enhanced the bat algorithm's (BA) efficacy by addressing issues like speed, accuracy, and local extremum entrapment.

Conclusions:

  • The developed AI-powered system offers a highly accurate and efficient solution for autism spectrum disorder (ASD) diagnosis.
  • This model has the potential to significantly assist clinicians and researchers in improving ASD diagnostic processes.
  • Early and accurate identification through this advanced method can lead to better management and improved life standards for individuals with ASD.