Abstract
There is growing evidence for the potential of machine learning techniques for brain-based diagnosis of psychiatric disorders. However, few studies have been conducted to predict the diagnosis of bipolar disorder (BD) based on structural connectivity. This study aims at using machine learning algorithms to identify biomarkers that can be used to classify BD patients from healthy controls (HC) and investigating whether clinical dimensions of BD affect the accuracy of the classification.
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