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 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).

Source: Schizophrenia Research - December 21, 2017 Category: Psychiatry Authors: Julie L. Winterburn, Aristotle N. Voineskos, Gabriel A. Devenyi, Eric Plitman, Camilo de la Fuente-Sandoval, Nikhil Bhagwat, Ariel Graff-Guerrero, Jo Knight, M. Mallar Chakravarty Source Type: research