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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

192
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.
192
Learning Disabilities01:25

Learning Disabilities

264
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
264

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Related Experiment Video

Updated: Aug 29, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning.

N V L M Krishna Munagala1, V Saravanan2, Firas Husham Almukhtar3

  • 1Department of Electrical Electronics and Communication Engineering, GITAM Institute of Technology, GITAM Deemed University, Visakhapatnam, Andhra Pradesh 530045, India.

Computational Intelligence and Neuroscience
|September 6, 2022
PubMed
Summary
This summary is machine-generated.

A new method effectively diagnoses Autism Spectrum Disorder (ASD) by addressing data noise and imbalance. This approach improves classification accuracy for neurodevelopmental disorder diagnosis.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Informatics

Background:

  • Autism Spectrum Disorder (ASD) presents complex neurodevelopmental challenges.
  • Existing classification algorithms struggle with ASD data characteristics like label noise, high dimensionality, and class imbalance.
  • Current diagnostic systems are often binary and fail to capture the spectrum's complexity.

Purpose of the Study:

  • To develop an improved classification method for Autism Spectrum Disorder (ASD).
  • To address limitations in existing algorithms, specifically data label noise and sample imbalance.
  • To enhance the accuracy and effectiveness of ASD diagnosis.

Main Methods:

  • Utilized Label Distribution Learning (LDL) to manage noisy data labels.
  • Employed Support Vector Regression (SVR) for handling imbalanced sample data.
  • Implemented a cost-sensitive approach to correct sample imbalance.
  • Applied LDL to overcome high-dimensional feature classification challenges by mapping samples to a feature space for multiclass ASD diagnosis.

Main Results:

  • The proposed method effectively balances the influence of majority and minority classes.
  • Demonstrated significant improvement in handling imbalanced data crucial for ASD diagnosis.
  • Outperformed previous methods in classification performance and accuracy.
  • Successfully resolved issues related to unbalanced data in ASD diagnostic contexts.

Conclusions:

  • The novel approach offers a more robust solution for ASD diagnosis by tackling inherent data complexities.
  • This method enhances diagnostic accuracy and reliability for Autism Spectrum Disorder.
  • The strategy provides a valuable tool for improving the classification and understanding of neurodevelopmental disorders like ASD.