A Machine Learning fMRI Approach in the Diagnosis of Autism
| Autor: | Kostakis Gkiatis, Aikaterini S. Karampasi, Panteleimon Asvestas, Ioannis Zorzos, Ioannis Kakkos, Georgios N. Dimitrakopoulos, George K. Matsopoulos, Stavros-Theofanis Miloulis |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
medicine.diagnostic_test Computer science business.industry Feature selection medicine.disease Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Discriminative model Categorization Autism spectrum disorder Taxonomy (general) medicine Task analysis Autism Artificial intelligence business Functional magnetic resonance imaging computer 030217 neurology & neurosurgery 030304 developmental biology |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata50022.2020.9378453 |
| Popis: | Diagnosis of Autism Spectrum Disorder (ASD) is a complex task that typically relies on the expertise of the clinician due to the lack of specific quantitative biomarkers. As a consequence, automatic categorization of an individual within the ASD taxonomy poses many challenges, usually with controversial results. The implementation of Machine Learning approaches as a diagnostic tool for ASD classification is rapidly growing in the field of neuroscience, holding the potential to enhance discrimination validity among ASD and Typically Developed (TD) individuals, while providing indications in regard to ASD differentiating factors. In this study, various feature selection and classification techniques were employed in order to successfully discern between ASD and TD, using data from large resting-state functional Magnetic Resonance Imaging (rs-fMRI) database. Moreover, we adopt novel features, namely the Haralick texture features and the Kullback-Leibler divergence, combined with already established ones (i.e. static Functional Connectivity and demographics), assessing the most informative global attributes. Our framework succeeded in the identification of a small number of discriminative features, leading to high performance relative to previous works with optimal classification accuracy of 0.725. |
| Databáze: | OpenAIRE |
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