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Autism Spectrum Disorder

Autism Spectrum Disorder

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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Updated: Jun 12, 2025

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

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Published on: April 22, 2015

HCBiLSTM-WOA: hybrid convolutional bidirectional long short-term memory with water optimization algorithm for autism

V Kavitha¹, R Siva¹

¹Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.

Computer Methods in Biomechanics and Biomedical Engineering

|September 18, 2024

Summary

A new Hybrid Convolutional Bidirectional Long Short-Term Memory based Water Optimization Algorithm (HCBiLSTM-WOA) effectively detects Autism Spectrum Disorder (ASD) in real-time datasets. This advanced method achieves 98.53% accuracy, enabling earlier diagnosis and intervention for individuals with ASD.

Keywords:

Autism spectrum disorder Convolutional neural network Hybrid convolutional bidirectional long short-term memory Hyperparameter Water optimization algorithm

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Published on: December 14, 2012

Area of Science:

Neurology and Artificial Intelligence
Developmental Neuroscience
Machine Learning in Healthcare

Background:

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disability requiring early intervention for optimal outcomes.
Existing ASD detection methods are often cumbersome and insufficient for real-time data analysis.
Accurate and timely diagnosis is crucial for managing ASD's impact on individuals' physical and mental health.

Purpose of the Study:

To propose a novel and efficient mechanism for Autism Spectrum Disorder (ASD) detection using real-time datasets.
To enhance the accuracy and efficiency of ASD classification through a hybrid deep learning and optimization algorithm approach.
To develop a tool that aids in the early and precise detection of ASD across various age groups.

Main Methods:

A Hybrid Convolutional Bidirectional Long Short-Term Memory (HCBiLSTM) model was developed for ASD classification.
The Water Optimization Algorithm (WOA) was employed to tune the hyperparameters of the HCBiLSTM model, optimizing prediction accuracy.
Real-time ASD datasets encompassing diverse age groups were preprocessed to handle missing values, outliers, and data reduction before classification.

Main Results:

The proposed HCBiLSTM-WOA method achieved a high ASD prediction accuracy of approximately 98.53%.
The method demonstrated superior performance in detecting ASD compared to existing techniques across various performance metrics, including accuracy, sensitivity, and specificity.
The model successfully classified ASD cases into respective stages based on observed autistic traits.

Conclusions:

The fusion of HCBiLSTM and WOA offers a robust framework for accurate and efficient ASD diagnosis.
This approach enhances diagnostic precision and improves the interpretation of complex ASD data, supporting early intervention strategies.
The HCBiLSTM-WOA method provides clinicians with a valuable tool for early and precise ASD detection, ultimately improving patient outcomes.