Robust missing data reconstruction in schizophrenia using tracking-removed autoencoder with fuzzy confidence integration.

ABSTRACT: Neural network models for outcome prediction play a pivotal role in neurological disease research, particularly for baseline risk assessment. Schizophrenia, a complex and relatively rare neuropsychiatric disorder, presents significant diagnostic challenges due to its heterogeneous symptomatology and reliance on subjective clinical evaluations. One of the primary barriers to accurate modelling in Schizophrenia research is the prevalence of incomplete clinical data, often stemming from inconsistent reporting, participant dropout, and variability in assessment protocols. To address these limitations, an advanced deep learning framework has been developed that integrates the Tracking-Removed Autoencoder (TRAE) with Multi-View Progressive Training (MVPT). This architecture is specifically desig

SUBMITTER: Mateen M 

PROVIDER: S-EPMC12630915 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature