Volume 20 No 22 (2022)
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Machine Learning Approaches for Early Autism Spectrum Disorder Detection in Children
Sathiyakeerthi Madasamy
A complicated neurodevelopmental illness called autism spectrum disorder (ASD) impacts people's behavior and social interaction. Early and accurate diagnosis of ASD is crucial for early intervention and improved long-term outcomes. In recent years, machine learning techniques have emerged as promising tools for ASD classification. This research paper aims to explore the classification of autism using two popular machine learning algorithms: Support Vector Machines (SVM) and Random Forest (RF). The study compares the performance of SVM and RF in accurately identifying individuals with ASD based on a set of relevant features. The results demonstrate the effectiveness of these algorithms in autism classification, highlighting their potential as valuable tools for aiding clinical diagnosis.
Machine learning, Autism detection, Autism classification, Outlier detection, Feature extraction
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