Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.

ABSTRACT: A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VB

SUBMITTER: Salvador R 

PROVIDER: S-EPMC5398548 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature