Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that requires timely and accurate detection for effective intervention. This study evaluates the performance of six machine learning models—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, XGBoost, and k-Nearest Neighbors (kNN)-in detecting ASD across three age groups, using three separate datasets: children, adolescents, and adults. Using a comprehensive dataset segmented by age, each model was assessed through stratified 10-fold cross-validation based on five key performance metrics: accuracy, precision, recall, F1 score, and area under the curve (AUC). The findings indicate that ensemble and linear models, particularly XGBoost and Logistic Regression, consistently deliver the most reliable results across all age groups and the combined dataset, with high classification accuracy and balanced precision-recall tradeoffs. In contrast, kNN and Decision Tree models displayed inconsistent performance, often struggling with both false positives and false negatives. This analysis supports the application of advanced machine learning methods, especially ensemble techniques, for developing robust and generalizable ASD detection systems across diverse age demographics.
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