| Title: |
Comparison of Machine Learning Methods for Classification of Alexithymia in Individuals With and Without Autism from Eye-Tracking Data |
| Authors: |
IIgin, Furkan; Witherow, Megan A.; Iftekharuddin, Khan M. |
| Source: |
Electrical & Computer Engineering Faculty Publications |
| Publisher Information: |
ODU Digital Commons |
| Publication Year: |
2023 |
| Collection: |
Old Dominion University: ODU Digital Commons |
| Subject Terms: |
Alexithymia; Autism; Data modeling; Decision trees; Education and training; Emotion; Eye models; Eye tracking; Imbalanced data; Lawrencium; Machine learning; Neurological disorders; Oversampling; Performance modeling; Support vector machine; Artificial Intelligence and Robotics; Communication Sciences and Disorders; Neurology |
| Description: |
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. |
| Document Type: |
conference object |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
https://digitalcommons.odu.edu/ece_fac_pubs/424; https://digitalcommons.odu.edu/context/ece_fac_pubs/article/1433/viewcontent/Iftekharruddin_2023_ComparisonofMachineLearningMethodsforClassificationofAlexithymiainIndividualswithandWithoutAutismOCR.pdf |
| DOI: |
10.1117/12.2682724 |
| Availability: |
https://digitalcommons.odu.edu/ece_fac_pubs/424; https://doi.org/10.1117/12.2682724; https://digitalcommons.odu.edu/context/ece_fac_pubs/article/1433/viewcontent/Iftekharruddin_2023_ComparisonofMachineLearningMethodsforClassificationofAlexithymiainIndividualswithandWithoutAutismOCR.pdf |
| Rights: |
Copyright 2023 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. |
| Accession Number: |
edsbas.73A8E6D4 |
| Database: |
BASE |