Comparing Techniques for Classifying Patients with Schizophrenia and Healthy Controls using Machine Learning and Magnetic Resonance Imaging

Schizophrenia and related psychoses are debilitating mental illnesses that are associated with an abnormal neuroanatomical phenotype on magnetic resonance images. Machine learning is a powerful statistical tool that can recognize unique feature patterns in data and use this information to perform class separation in unseen instances. This study explores the effect of machine learning algorithm selection, dataset characteristics, and feature choice on classification performance within a rigorous validation framework. Specifically, penalized logistic regression, support vector machines, and linear discriminant analysis are compared using three large, independently-collected datasets with multiple neuroanatomically-based features. This study replicates existing studies in the literature, and provides a direct comparison of techniques within a systematic structure. Additionally, this study illustrates many of the challenges inherent to performing patient classification, and aims to establish a benchmark for future studies in the field.M.A.S