Fusion of Multiple Self-Diagnostic Questionnaires into Optimal Diagnostic Cut-offs and Factor Analysis for Depression Characterization of the Korean University Student Group

Abstract Background: The diagnostic cut-offs and factor analysis prepared for a general population by depression self-questionnaires are inadequate for a focused population with its cultural background. Therefore, we present a novel approach to optimizing diagnostic cut-offs and generalizing factor analysis for the Korean university student group in the fusion space of multiple self-questionnaires. Methods: We collected the data from 30 randomly selected Korean university students, over 21 weeks, with the psychiatric evaluation as a reference, then established the optimal cut-off regions in the fused CESD−­PHQ9 score space based on the statistical correlation between CES−D and PHQ−9 and the reference diagnostics. We also re-extracted the factors in the fused CESD−PHQ9 space to expose the key factors that are behind the depression characteristics of the group. Results: The proposed approach is population-specific cut-off regions, which can provide more reliable and trustworthy diagnoses. On the extended factor analysis, we identified the major factors of the depression characteristics of the group to be the “socio-psychological” and “interpersonal relationship” associated with academic studies and employment. Limitations: Since there is a lack of evaluation of the accuracy of the proposed approach with the clinical diagnosis, whether it can effectively fulfill the role of intermediate diagnosis between patients and psychiatrists needs to be verified. Conclusion: We identified optimal cut-off regions and generalized factor analysis in the fusion space. Finally, these can serve as a self-diagnostic tool for reliably identifying the depression characteristics of a focused population as well as effectively linking individuals and psychiatrists as an intermediary.