Explainable Ensemble Learning Framework for Early Detection of Autism Spectrum Disorder: Enhancing Trust, Interpretability and Reliability in AI-Driven Healthcare

Abstract

Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types of datasets: (i) a standard behavioral dataset and (ii) a more complex multimodal dataset with images, audio, and physiological information. The datasets were carefully preprocessed for missing values, redundant features, and dataset imbalance to ensure fair learning. The results outperformed the state-of-the-art with a Regularized Neural Network, achieving 97.6% accuracy on behavioral data. Whereas, on the multimodal data, the accuracy is 98.2%. Other models also did well with accuracies consistently above 96%. We also used SHAP and LIME on a behavioral dataset for models' explainability.