Machine learning approach for Migraine Aura Complexity Score prediction based on magnetic resonance imaging data.

Autor: Mitrović K; Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac, 65 Svetog Save, Čačak, 32000, Serbia. kacam.ftn@gmail.com., Savić AM; Science and Research Centre, University of Belgrade - School of Electrical Engineering, University of Belgrade, 73 Bulevar kralja Aleksandra, Belgrade, 11000, Serbia., Radojičić A; Headache Center, Neurology Clinic, University Clinical Centre of Serbia, 6 dr Subotića starijeg, Belgrade, 11000, Serbia.; Faculty of Medicine, University of Belgrade, 8 dr Subotića starijeg, Belgrade, 11000, Serbia., Daković M; Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, 12-16 Studentski trg, Belgrade, 11000, Serbia., Petrušić I; Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, 12-16 Studentski trg, Belgrade, 11000, Serbia.
Jazyk: angličtina
Zdroj: The journal of headache and pain [J Headache Pain] 2023 Dec 18; Vol. 24 (1), pp. 169. Date of Electronic Publication: 2023 Dec 18.
DOI: 10.1186/s10194-023-01704-z
Abstrakt: Background: Previous studies have developed the Migraine Aura Complexity Score (MACS) system. MACS shows great potential in studying the complexity of migraine with aura (MwA) pathophysiology especially when implemented in neuroimaging studies. The use of sophisticated machine learning (ML) algorithms, together with deep profiling of MwA, could bring new knowledge in this field. We aimed to test several ML algorithms to study the potential of structural cortical features for predicting the MACS and therefore gain a better insight into MwA pathophysiology.
Methods: The data set used in this research consists of 340 MRI features collected from 40 MwA patients. Average MACS score was obtained for each subject. Feature selection for ML models was performed using several approaches, including a correlation test and a wrapper feature selection methodology. Regression was performed with the Support Vector Machine (SVM), Linear Regression, and Radial Basis Function network.
Results: SVM achieved a 0.89 coefficient of determination score with a wrapper feature selection. The results suggest a set of cortical features, located mostly in the parietal and temporal lobes, that show changes in MwA patients depending on aura complexity.
Conclusions: The SVM algorithm demonstrated the best potential in average MACS prediction when using a wrapper feature selection methodology. The proposed method achieved promising results in determining MwA complexity, which can provide a basis for future MwA studies and the development of MwA diagnosis and treatment.
(© 2023. The Author(s).)
Databáze: MEDLINE
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