The complex neurological condition known as autism spectrum
disorder (ASD) is characterized by repetitive behaviors, social
interaction, and communication. For immediate assistance and
support, early and accurate ASD prediction is essential. In this study,
we use a dataset of behavioral and clinical variables to assess how well
different machine learning (ML) algorithms predict ASD. The
algorithms analyzed include Decision Tree Classifier, Gaussian Naive
Bayes (GNB), XGBoost, K-Nearest Neighbors (KNN), LightGBM, and
CatBoost. Our findings show that sophisticated ensemble techniques
perform more accurately than conventional classifiers. With an
accuracy of 95.39%, the GNB classifier demonstrated a notable
improvement over the Decision Tree (DT) classifier, which had an
accuracy of 85.11%. The ensemble approaches XGBoost, LightGBM,
and CatBoost, however, achieved the highest accuracies, with
respective results of 97.87%, 97.16%, and 98.23%. With an accuracy of
93.26%, the KNN classifier likewise demonstrated strong performance.
These findings suggest that ensemble methods, particularly CatBoost,
provide superior predictive performance for ASD detection compared
to other algorithms. The confusion matrix analysis further supports the
robustness of these models by highlighting their precision and recall
metrics. According to the study’s findings, applying advanced machine
learning algorithms could significantly increase the predictive
accuracy of ASD, perhaps resulting in an earlier diagnosis and better
outcomes for those on the spectrum. Future studies should examine
how these models might be incorporated into therapeutic settings and
evaluate how applicable they are in the real world.
Sanju S. Anand1, Shashidhar Kini2
Srinivas University, India1, Srinivas Institute of Technology, India2
Autism Spectrum Disorder, Decision Tree, Naive Bayes (NB), XGBoost, K-Nearest Neighbors, Machine Learning, LightGBM, CatBoost, Predictive Modelling, Ensemble Methods
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 3 , Pages: 3509 - 3516 )
Date of Publication :
February 2025
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78
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