Comparison of machine learning methods for classification of alexithymia in individuals with and without autism from eye-tracking data

Furkan Ilgin; Megan A. Witherow; Khan M. Iftekharuddin

doi:10.1117/12.2682724

4 October 2023 Comparison of machine learning methods for classification of alexithymia in individuals with and without autism from eye-tracking data

Furkan Ilgin, Megan A. Witherow, Khan M. Iftekharuddin

Author Affiliations +

Proceedings Volume 12675, Applications of Machine Learning 2023; 126750P (2023) https://doi.org/10.1117/12.2682724
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Alexithymia describes a psychological state where individuals struggle with feeling and expressing their emotions. Individuals with alexithymia may also have a more difficult time understanding the emotions of others and may express atypical attention to the eyes when recognizing emotions. This is known to affect individuals with Autism Spectrum Disorder (ASD) differently than neurotypical (NT) individuals. Using a public data set of eye-tracking data from seventy individuals with and without autism who have been assessed for alexithymia, we train multiple traditional machine learning models for alexithymia classification including support vector machines, logistic regression, decision trees, random forest, and multilayer perceptron. To correct for class imbalance, we evaluate four different oversampling strategies: no oversampling, random oversampling, SMOTE, and ADASYN. We consider three different groups of data: ASD, NT, and combined ASD+NT. We use a nested leave-one-out cross validation strategy to perform hyperparameter selection and evaluate model performance. We achieve F1 scores of 90.00% and 51.85% using decision trees for ASD and NT groups, respectively, and 72.41% using SVM for the combined ASD+NT group. Splitting the data into ASD and NT groups improves recall for both groups compared to the combined model.

Conference Presentation

Citation Download Citation

Furkan Ilgin, Megan A. Witherow, and Khan M. Iftekharuddin "Comparison of machine learning methods for classification of alexithymia in individuals with and without autism from eye-tracking data", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750P (4 October 2023); https://doi.org/10.1117/12.2682724

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