Machine learning approach for Migraine Aura Complexity Score prediction based on magnetic resonance imaging data.
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) algorith...
| Publicado en: | Journal of Headache & Pain Vol. 24; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
12/18/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174266547&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174266547 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11292369 O3T jtl: Journal of Headache & Pain issn: 11292369 maglogo: N pubinfo: dt: 12/18/2023 vid: 24 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 174266547 174266547 174266547 10.1186/s10194-023-01704-z 174266547 ppf: 1 ppct: 12 formats: tig: atl: Machine learning approach for Migraine Aura Complexity Score prediction based on magnetic resonance imaging data. aug: au: Mitrović, Katarina Savić, Andrej M. Radojičić, Aleksandra Daković, Marko Petrušić, Igor affil: https://ror.org/04f7vj627 Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac, 65 Svetog Save, 32000, Čačak, Serbia sug: subj: Machine Learning Utilization Migraine Physiopathology Magnetic Resonance Imaging Epilepsy Neuroradiography Algorithms Human Male Female Linear Regression Support Vector Machine Spearman's Rank Correlation Coefficient Adult Descriptive Statistics Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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