SVM Optimization for Autism Spectrum Disorder Classification: A Comparison of PCA, PSO, and Grid Search

Aura Choirun Nisa; Basuki Rahmat; Achmad Junaidi

doi:10.59934/jaiea.v4i3.1044

Authors

Aura Choirun Nisa Universitas Pembangunan Nasional “Veteran” Jawa Timur
Basuki Rahmat Universitas Pembangunan Nasional “Veteran” Jawa Timur
Achmad Junaidi Universitas Pembangunan Nasional “Veteran” Jawa Timur

DOI:

https://doi.org/10.59934/jaiea.v4i3.1044

Keywords:

SVM, PCA, PSO, GridSearch, Autism Spectrum Disorder (ASD)

Abstract

Autism Spectrum Disorder (ASD) is a developmental condition impacting communication and socialization, often manifesting in distinct behaviors. Early detection and timely intervention are crucial for improving the quality of life for individuals with ASD. This research aims to develop an ASD risk classification model using the Support Vector Machine (SVM) algorithm across three age groups: children, adolescents, and adults. To optimize model performance, Principal Component Analysis (PCA) was used for dimensionality reduction, while Particle Swarm Optimization (PSO) and Grid Search were employed for parameter tuning. The study sought to identify the most effective combination of these techniques for autism prediction. Evaluation results indicated that SVM with Grid Search optimization, without PCA, yielded the best performance, achieving 98.2% accuracy and an AUC of 0.997 at an 80:20 data split. Furthermore, Grid Search demonstrated greater computational efficiency compared to PSO. The findings suggest that the integration of SVM and Grid Search offers a promising, accurate, and efficient approach for the early detection of autism.

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Author Biography

Basuki Rahmat, Universitas Pembangunan Nasional “Veteran” Jawa Timur

Vice Dean of Computer Science Faculty on Universitas Pembangunan Nasional "Veteran" Jawa Timur

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