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Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning?:A multi-method and multi-dataset study

Winterburn, Julie L and Voineskos, Aristotle N and Devenyi, Gabriel A and Plitman, Eric and de la Fuente-Sandoval, Camilo and Bhagwat, Nikhil and Graff-Guerrero, Ariel and Knight, Jo and Chakravarty, M Mallar (2017) Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning?:A multi-method and multi-dataset study. Schizophrenia Research. ISSN 0920-9964

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    Abstract

    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.

    Item Type: Journal Article
    Journal or Publication Title: Schizophrenia Research
    Additional Information: This is the author’s version of a work that was accepted for publication in Schizophrenia Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Schizophrenia Research ??, ?, 2018 DOI: 10.1016/j.schres.2017.11.038
    Subjects: ?? STRUCTURAL MAGNETIC RESONANCE IMAGINGMACHINE LEARNINGCLASSIFICATIONSCHIZOPHRENIAVOXEL-BASED MORPHOMETRYCORTICAL THICKNESS ??
    Departments: Faculty of Health and Medicine > Medicine
    ID Code: 89466
    Deposited By: ep_importer_pure
    Deposited On: 05 Jan 2018 15:22
    Refereed?: Yes
    Published?: Published
    Last Modified: 30 Apr 2019 02:41
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/89466

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