Psychological Distress Prediction Based on Maladaptive Cognitive Schemas and Anxiety with Random Forest Regression Algorithm

Abstract

Psychological distress represents a complex and pervasive concern impacting individuals globally, characterized by a wide spectrum of emotional, cognitive, and physiological experiences. This multifaceted phenomenon is frequently intertwined with the presence of maladaptive cognitive schemas and heightened levels of anxiety, both recognized as contributing factors. Accurate prediction of psychological distress is of paramount significance for clinicians, researchers, and healthcare practitioners as it can drive early interventions, and personalized treatment plans, and optimize resource allocation. This research delves into the predictive capabilities of maladaptive cognitive schemas and anxiety in the context of psychological distress, employing the Random Forest Regression (RFR) algorithm. The RFR algorithm, a powerful ensemble learning method, offers the potential to comprehensively explore the intricate interplay of variables and predictors, enhancing the precision of psychological distress prediction. By harnessing the capabilities of this advanced algorithm, we seek to provide a more robust framework for understanding, assessing, and addressing psychological distress. This research aspires to illuminate the predictive potential of maladaptive cognitive schemas and anxiety, thereby contributing to the development of more effective early interventions and personalized treatment strategies. Ultimately, this study holds the promise of significantly improving our capacity to predict and intervene in cases of psychological distress, ultimately enhancing the well-being of individuals and the efficiency of healthcare delivery. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, tweak, and build upon the work non commercially, as long as the author is credited and the new creations are licensed under the identical terms.