Explainable and secure framework for autism prediction using multimodal eye tracking and kinematic data

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by difficulties in social skills, repetitive behaviours, and communication. Early and accurate diagnosis is essential for effective intervention and support. This paper proposes a secure and privacy-preserving framework for diagnosing ASD by integrating multimodal kinematic and eye movement sensory data, Deep Neural Networks (DNN), and Explainable Artificial Intelligence (XAI). Federated Learning (FL), a distributed machine learning approach, is utilized to ensure data privacy by training models across multiple devices without centralizing sensitive data. In our evaluation, we employ FL using a shallow DNN as the shared model and Federated Averaging (FedAvg) as the aggregation algorithm. We conduct experiments across two scenarios for each dataset: the first using FL with all features and the second using FL with features selected by XAI. The experiments, conducted with three clients over three rounds of training, show that the L_General dataset produces the best results, with Client 2 achieving an accuracy of 99.99% and Client 1 achieving 88%. This study underscores FL’s potential to preserve privacy and security while maintaining high diagnostic accuracy, making it a viable solution for healthcare applications involving sensitive data.