Autism Spectrum Disorder Prediction for Children's Using Convolutional Neural Networks
Year: 2025
Type: article
Abstract: This article introduces the techniques to identify. Use help to classify and characterize some ASD data. Machine learning technology. Machine Learning Algorithms are important to improve diagnosis accuracy and timing. Work is accurately compared. It emphasizes the efficiency of other algorithms. Methods such as Random Forest, support vector machine, KNearest Neighbor, Gradient Reinforcement Machine, logistic regression, and deep neural networks. Comparison of this result shows that CNN methods have high accuracy. Diagnosis of autism spectrum disorder (ASD) is still a challenging topic, especially in regions where access to professionals is limited. Usually, people's gaze patterns are recorded when looking at an intended image to compare and determine whether a child has autism. The results show that the proposed method has the highest accuracy.
Author: V, Mahalakshmi
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6911901666
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