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

• Published in: Journal of headache and pain Vol. 24; no. 1; pp. 169 - 12
• Main Authors: Mitrović, Katarina, Savić, Andrej M., Radojičić, Aleksandra, Daković, Marko, Petrušić, Igor
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 18.12.2023; Springer Nature B.V; BMC
• Subjects: Algorithms; Artificial intelligence; Feature selection; Headache; Internal Medicine; Learning algorithms; Machine learning; Magnetic resonance imaging; Medicine; Medicine & Public Health; Migraine; Migraine with Aura; Neuroimage on Headache; Neuroimaging; Neurology; Pain Medicine; Pathophysiology; Prediction, regression; Support vector machine; Support vector machines; Support vector machine; Magnetic resonance imaging; Migraine with Aura; Artificial intelligence; Prediction, regression; Machine learning
• ISSN: 1129-2377; 1129-2369; 1129-2377
• DOI: 10.1186/s10194-023-01704-z

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.