Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data.
Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kin...
| Publicado en: | Brain Imaging & Behavior Vol. 13; no. 4; pp. 1103 - 1115 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2019
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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=137507330&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137507330 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Aug2019 vid: 13 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137507330 137507330 NLM29992392 137507330 10.1007/s11682-018-9926-9 NLM29992392 137507330 ppf: 1103 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data. aug: au: Saccà, Valeria Sarica, Alessia Novellino, Fabiana Barone, Stefania Tallarico, Tiziana Filippelli, Enrica Granata, Alfredo Chiriaco, Carmelina Bruno Bossio, Roberto Valentino, Paola Quattrone, Aldo affil: Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy sug: subj: Brain Mapping Methods Multiple Sclerosis Forecasting Male Magnetic Resonance Imaging Methods Cognition Probability Algorithms Relaxation Adult Brain Female Middle Age Clinical Assessment Tools Scales Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kinds of Machine Learning technique could be effective to support early diagnosis of Multiple Sclerosis and which of them presented best performance in distinguishing Multiple Sclerosis patients from control subjects. We selected following algorithms: Random Forest, Support Vector Machine, Naïve-Bayes, K-nearest-neighbor and Artificial Neural Network. We applied the Independent Component Analysis to resting-state functional-MRI sequence to identify brain networks. We found 15 networks, from which we extracted the mean signals used into classification. We performed feature selection tasks in all algorithms to obtain the most important variables. We showed that best discriminant network between controls and early Multiple Sclerosis, was the sensori-motor I, according to early manifestation of motor/sensorial deficits in Multiple Sclerosis. Moreover, in classification performance, Random Forest and Support Vector Machine showed same 5-fold cross-validation accuracies (85.7%) using only this network, resulting to be best approaches. We believe that these findings could represent encouraging step toward the translation to clinical diagnosis and prognosis. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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