  Autistic Spectrum Disorder (ASD) is a neurological disease characterized by
difficulties with social interaction, communication, and repetitive activities.
While its primary origin lies in genetics, early detection is crucial, and
leveraging machine learning offers a promising avenue for a faster and more
cost-effective diagnosis. This study employs diverse machine learning methods
to identify crucial ASD traits, aiming to enhance and automate the diagnostic
process. We study eight state-of-the-art classification models to determine
their effectiveness in ASD detection. We evaluate the models using accuracy,
precision, recall, specificity, F1-score, area under the curve (AUC), kappa,
and log loss metrics to find the best classifier for these binary datasets.
Among all the classification models, for the children dataset, the SVM and LR
models achieve the highest accuracy of 100% and for the adult dataset, the LR
model produces the highest accuracy of 97.14%. Our proposed ANN model provides
the highest accuracy of 94.24% for the new combined dataset when
hyperparameters are precisely tuned for each model. As almost all
classification models achieve high accuracy which utilize true labels, we
become interested in delving into five popular clustering algorithms to
understand model behavior in scenarios without true labels. We calculate
Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Silhouette
Coefficient (SC) metrics to select the best clustering models. Our evaluation
finds that spectral clustering outperforms all other benchmarking clustering
models in terms of NMI and ARI metrics while demonstrating comparability to the
optimal SC achieved by k-means. The implemented code is available at GitHub.
