Psychological Resilience Analysis under the Perspective of Positive Psychology Based on Data Mining Modeling

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Shaojing Wang, Meng Zhang


This study investigates psychological resilience via the lens of Positive Psychology, employing advanced data mining modeling approaches, with a focus on the Random Forest method. Psychological resilience, defined as the ability to recover from hardship and retain well-being, is critical for an individual's mental health and overall success. Using Positive Psychology concepts, which highlight human strengths and virtues, this study seeks to reveal the multidimensional nature of resilience and find underlying factors that contribute to resilience outcomes. The Random Forest technique, which is well-known for its ability to handle large datasets and derive significant insights, serves as the computational foundation for this study. This study aims to move the focus of resilience research from pathology to strengths-based methods by combining Positive Psychology viewpoints with Random Forest modeling, stressing adaptability, growth, and thriving in the face of adversity. This study aims to identify patterns, and connections, and predict factors influencing individuals' resilience levels by analyzing large-scale datasets containing a variety of psychosocial characteristics such as personality traits, coping mechanisms, and social support networks. The study's findings show potential for guiding evidence-based treatments aimed at boosting resilience and well-being in individuals and communities, ultimately leading to greater thriving and fulfilment in the face of life's obstacles.

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