Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study.
Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validation techniques. Here, we perform a critical apprai...
| Publicado en: | Schizophrenia Research Vol. 214; pp. 3 - 11 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Dec2019
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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=141109794&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141109794 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09209964 3PT jtl: Schizophrenia Research issn: 09209964 maglogo: N pubinfo: dt: Dec2019 vid: 214 pid: 1004 pub: Elsevier B.V. artinfo: ui: 141109794 141109794 NLM29274736 141109794 10.1016/j.schres.2017.11.038 NLM29274736 141109794 ppf: 3 ppct: 8 formats: tig: atl: Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study. aug: au: Winterburn, Julie L. Voineskos, Aristotle N. Devenyi, Gabriel A. Plitman, Eric de la Fuente-Sandoval, Camilo Bhagwat, Nikhil Graff-Guerrero, Ariel Knight, Jo Chakravarty, M. Mallar affil: Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada sug: subj: Magnetic Resonance Imaging Methods Brain Schizophrenia Classification Schizophrenia Schizophrenia Pathology Human Male Data Collection Image Interpretation, Computer Assisted Methods Adult Brain Pathology Body Weights and Measures Female Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Adult: 19-44 years Male Female ab: Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validation techniques. Here, we perform a critical appraisal of the accuracy of machine learning methodologies used in SZ/HC classifications studies by comparing three machine learning algorithms (logistic regression [LR], support vector machines [SVMs], and linear discriminant analysis [LDA]) on three independent datasets (435 subjects total) using two tissue density estimates and cortical thickness (CT). Performance is assessed using 10-fold cross-validation, as well as a held-out validation set. Classification using CT outperformed tissue densities, but there was no clear effect of dataset. LR, SVMs, and LDA each yielded the highest accuracies for a different feature set and validation paradigm, but most accuracies were between 55 and 70%, well below previously reported values. The highest accuracy achieved was 73.5% using CT data and an SVM. Taken together, these results illustrate some of the obstacles to constructing effective disease classifiers, and suggest that tissue densities and CT may not be sufficiently sensitive for SZ/HC classification given current available methodologies and sample sizes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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