Construction of a Big Data-Driven Predictive Analysis Platform for Hospital Talent Attrition

Abstract

Objective: This study develops a big data-driven predictive platform for hospital staff attrition, integrating machine learning (ML) with psychological constructs. Negotiable Fate (NF), a culturally rooted belief system, is examined as a predictor of turnover via psychological capital (PC) and organizational citizenship. Methods: Structured HR data from 400+ employees at a tertiary public hospital, covering 20+ features, were analyzed. Due to attrition imbalance (~5%), SMOTE was applied to balance the dataset. Four ML classifiers-logistic regression, decision tree, random forest, and XGBoost-were evaluated using accuracy, precision, recall, and F1-score. Statistical analyses assessed mediation, moderation, and construct validity using survey variables: NF, PC, perceived organizational support, job performance (JP), and organizational citizenship behavior. Results: < 0.001), confirming mediated moderation. Conclusion: Integrating ML with psychological theory enhances both the prediction and understanding of hospital staff attrition. The platform enables culturally sensitive, data-driven HR interventions, helping administrators identify high-risk employees and implement targeted strategies to reduce attrition, stabilize the workforce, and improve patient care.

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