Cover
Vol. 2 No. 2 (2026)

Published: June 30, 2026

Pages: 47-58

Research Article

Early Detection of Autism Spectrum Disorder in Children Using Different Machine Learning Algorithms

Sabreen Kadhum 1 Mohammed Ali Tawfeeq 2
1 Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq
2 Artificial Intelligence Engineering Department, College of Engineering, Al-Naji University, Baghdad, Iraq
History
  • Received: June 3, 2026
  • Revised: June 25, 2026
  • Accepted: June 26, 2026
  • Available Online: June 30, 2026
DOI

https://doi.org/10.65505/ETES-26-0009

Abstract

Autism spectrum disorder (ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in individuals. Global health organization facing difficulties in establishing an effective ASD diagnostic system that facilitates precise analysis and early autism prediction. It is a scientific issue that necessitates resolution. This research presents an approach for the early prediction of children with ASD utilizing significant variables through machine learning (ML) methods. Three stages comprise the suggested technique. First, a 1250-case ASD dataset was identified and preprocessed. Five extremely effective traits with high Pearson correlation coefficient (PCC) are chosen from 10 chosen ASD feature dataset through its paces using five ML techniques: Naive Bayes (NB), K-Nearest Neighbor (kNN), Decision Tree(DT), Support Vector Machine (SVM), and AdaBoostM1 (ABM1). The proposed framework is assessed in the third phase utilizing five measurements such as accuracy, precision, predicting time ,recall, and F1-score, . The findings revealed that: NB and K-NN approaches exhibit superior accuracy rates of 99.2% and 97.2%, with minimal prediction times of approximately 0.3 and 0.45 seconds, correspondingly. Conversely, the DT and AdBM1 methods demonstrate a minor decline in accuracy, achieving 94.8% and 87.6%, respectively, along with increased prediction times. Nonetheless, the SVM approach exhibits the least performance, achieving an accuracy of 80.4% with prediction time of 0.84 seconds. This research aligns with several Sustainable Development Goals (SDGs), including ٍSDG3, which focuses on good health, SCG10, which addresses reducing inequalities, including those related to disability. and SCG9 by strengthening technological capabilities, particularly in scientific research

Keywords

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