This work presents an approach to help with the autism diagnosis using Machine Learning and eye-tracking. The key idea is to learn the visual patterns of eye-tracking scanpaths, and hence the diagnosis can be approached as an image classification task. Our experimental results demonstrated that the visual representation could simplify the prediction problem, and attain a high accuracy as well. With simple neural network models, our approach could realize quite promising accuracy (AUC≈91.5%).
https://mahmoud-elbattah.github.io/ML4Autism/
Publications:
Elbattah, M., Carette, R. ,Dequen, G., Guérin, J, & Cilia, F. (2019, July). Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencoder. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. https://ieeexplore.ieee.org/document/8856904
Carette, R., Elbattah, M., Dequen, G., Guérin, J, & Cilia, F. (2019, February). Learning to predict autism spectrum disorder based on the visual patterns of eye-tracking scanpaths. In Proceedings of the 12th International Conference on Health Informatics (HEALTHINF 2019). https://www.researchgate.net/publication/331784416_Learning_to_Predict_Autism_Spectrum_Disorder_based_on_the_Visual_Patterns_of_Eye-tracking_Scanpaths
Carette, R., Elbattah, M., Dequen, G., Guérin J.L., & Cilia F. (2018, September). Visualization of eye-tracking patterns in autism spectrum disorder: method and dataset. In Proceedings of the 13th International Conference on Digital Information Management (ICDIM 2018).IEEE.