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

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is manifested by sensory abnormalities such as hypersensitivity to sound and touch. Autistic children often have problems with communication, social interaction, and behavioral patterns, which are also affected by media or cartoon characters, sometimes leading to unpredictable or dangerous behavior. Timely intervention and detection are important for enhancing development. In this study, we propose a data-driven machine learning (ML) framework to detect early autism in children. The proposed approach starts with overall data processing, which involves the handling of missing values, categorical data processing, and feature selection based on Information Gain and Pearson Correlation to identify the most important attributes. The Synthetic Minority Oversampling Technique (SMOTE) is used to overcome the imbalance between classes. The proposed model uses a stacked ensemble approach in which KNN, RF, SVM, NB, and DT are used as base learners, while Random Forest works as the meta-classifier. Hyper parameter optimization is performed to further optimize the performance of the model. The models are evaluated using accuracy, precision, recall, and F1-score. The experimental outcomes revealed that the proposed ensemble model is more effective than individual classifiers, achieving 99% accuracy on the Toddler Saudi dataset, 98% accuracy on Q-CHAT, and 99% accuracy on the Nao and fused datasets. Moreover, the Shapley explanation method is applied to determine the importance of features and the impact of these features on the model predictions. The results indicate that the proposed framework may assist healthcare workers in autism screening and decision-making, serving as a promising and efficient alternative means of early autism detection.