Schizophrenia and Bipolar Psychosis Classification with rsfMRI Functional Connectivity Feature Fusion technique using Super Learner

Schizophrenia and bipolar psychosis are intricate mental disorders that share similar clinical characteristics, making it difficult to establish precise diagnoses and classifications. In recent times, neuroimaging has displayed the potential to deepen our comprehension of these disorders. This study presents an enhanced framework to classify schizophrenia (SZ) and bipolar psychosis (BP) by combining resting-state functional magnetic resonance imaging (rsfMRI) functional connectivity and statistical features. Merging information of Intrinsic Connectivity Networks (ICN), Functional Network Connectivity (FNC), Kurtosis (K), and Power Spectral Density (PSD) details improve the classifying ability of the weighted voting super learner. The experimental results demonstrate that the proposed framework outperforms existing methods in terms of both prediction accuracy and computational efficiency with a mean gain of 6%. Moreover, the proposed technique achieves high classification performance while maintaining a low memory footprint and computational cost, making it ideal to classify the two identical overlapping classes with good precision values.