Volume V | Issue 1 | 2026 | Paper ID: IJATEM-VO5I01P2

Machine Learning model for Autism Spectrum Disorder Detection using EEG signal

Author(s)

A. Arockia Helen sushma, S. Priyadharshini, S. Nagakumar raj, A. Kayalvizhi

Cite This

Arockia Helen sushma. A, S. PriyaDharshini, S. Nagakumar raj, A. Kayalvizhi, "Machine Learning model for Autism Spectrum Disorder Detection using EEG signal", International Journal of Advances in Engineering and Management, vol. 5, no. 1, pp. 14-22, 2026.

Abstract

Diagnosing autism spectrum disorder (ASD) in youngsters presents considerable difficulties owing to the disorder's intricacy and unpredictability. Conventional diagnostic methods frequently depend on single-modal data, which can be restrictive and may not produce optimal outcomes. To address these limitations, we present a novel diagnostic methodology that utilizes the integration of electroencephalogram (EEG) data for enhanced ASD detection. The proposed study uses machine learning algorithms, mainly logistic regression (LRC) and extra tree classifier (ETC), to find correlations and new insights from data that is hidden in a latent feature space. Machine learning classifier methods generate optimal feature representations that enhance discriminability and generalization, ultimately improving diagnostic accuracy. The EEG dataset evaluated with ASD indicates that our approach markedly surpasses current methodologies. This innovation has significant potential for equipping physicians with a more objective and dependable tool for diagnosing autism spectrum disorder in children.

Keywords

ASD, EEG, ML, Logistic Regression classifier and Extra tree classifier

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