Enhance autism spectrum disorder detection using stacking ensemble learning model with explainable AI

This study proposes a machine learning framework combining multiple classifiers (KNN, Random Forest, SVM, Naive Bayes, Decision Tree) to improve early autism detection in children. The stacked ensemble approach achieved high accuracy rates (98-99%) on multiple datasets while using explainable AI techniques to identify important diagnostic features.