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Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis.

Ammar I Shihab¹, Faten A Dawood¹, Ali H Kashmar¹

¹Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq.

Advances in Bioinformatics

|May 13, 2020

Summary

This summary is machine-generated.

This study introduces a novel method for classifying autism spectrum disorder (ASD) in adults and children. Practical component analysis achieves high accuracy in identifying ASD, aiding in early diagnosis and intervention.

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Area of Science:

Neuroscience
Developmental Psychology
Data Science

Background:

Autism spectrum disorder (ASD) is a complex early developmental disorder affecting social interaction and communication.
Current data analysis and classification of ASD remain challenging due to the wide spectrum of signs and symptoms.
Understanding the neural underpinnings of ASD requires advanced analysis of responses to various stimuli.

Purpose of the Study:

To develop and evaluate a method for classifying autism spectrum disorder (ASD) in both adults and children.
To analyze neuroscientific data, specifically responses to audio and video stimuli, from individuals with ASD.
To improve the accuracy and efficiency of ASD diagnosis through data-driven approaches.

Main Methods:

The study employed practical component analysis (PCA) for data analysis and unsupervised classification.
A three-stage methodology was implemented: dataset preparation, data analysis, and unsupervised classification.
Neuroscientific data from adults and children with ASD, responding to audio-visual stimuli, were analyzed.

Main Results:

The classification model achieved a sensitivity of 78.6% and specificity of 82.47% for adults with ASD.
For children with ASD, the model demonstrated higher accuracy with a sensitivity of 87.5% and specificity of 95.7%.
The results indicate the effectiveness of the proposed method in differentiating ASD in different age groups.

Conclusions:

The practical component analysis method shows significant promise for the accurate classification of autism spectrum disorder.
The developed approach offers a valuable tool for aiding in the diagnosis of ASD in both pediatric and adult populations.
Further research can refine this method for earlier and more precise identification of ASD.