Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.
Background: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machin...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 17; pp. 1 - 11 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
4/13/2017
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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=122521199&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122521199 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/13/2017 vid: 17 pid: 24147 pub: BioMed Central artinfo: ui: 122521199 122521199 NLM28407777 122521199 10.1186/s12911-017-0434-4 NLM28407777 122521199 ppf: 1 ppct: 10 formats: tig: atl: Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data. aug: au: Garcia-Chimeno, Yolanda Garcia-Zapirain, Begonya Gomez-Beldarrain, Marian Fernandez-Ruanova, Begonya Garcia-Monco, Juan Carlos affil: DeustoTech - Fundacion Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain sug: subj: Migraine Diagnosis Migraine Classification Emotions Policy Making Headache Female Magnetic Resonance Imaging Neuropsychological Tests Middle Age Diagnosis, Computer Assisted Adult Migraine Psychosocial Factors Male Information Science Algorithms Human Middle Aged: 45-64 years Adult: 19-44 years Female Male ab: Background: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions.Methods: We select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms.Results: When classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions).Conclusions: The proposed feature selection committee method improved the performance of migraine diagnosis classifiers compared to individual feature selection methods, producing a robust system that achieved over 90% accuracy in all classifiers. The results suggest that the proposed methods can be used to support specialists in the classification of migraines in patients undergoing magnetic resonance imaging. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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