Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization.

Objective: To develop and validate predictive models for healthcare workforce transition success under decentralization using Support Vector Machine (SVM) analysis and to identify key determinants across organizational support domains. Methods: A cross-sectional study was conducted among 430 healthcare personnel transferred from Ministry of Public Health facilities to Provincial Administrative Organizations in Thailand (2023-2024). Thirty-seven predictors, including demographics, benefits, and welfare domains, were analyzed. Four kernel functions were compared using 10-fold cross-validation, and feature importance was assessed. Class imbalance was addressed with the Synthetic Minority Oversampling Technique (SMOTE). Results: The linear kernel achieved superior cross-validated performance (accuracy: 69 ± 4%, sensitivity: 46 ± 5%, specificity: 82 ± 4%, AUC: 0.64). SMOTE improved sensitivity to 54 ± 5% while maintaining specificity at 79 ± 5%. Five stable predictors were identified across validation folds: competitive compensation (0.427), career development opportunities (0.358), fair promotion processes (0.336), hazardous work compensation (0.285), and educational leave opportunities (0.252). Comparative analysis showed that SVM outperformed logistic regression (66% accuracy), random forest (66%), and gradient boosting (65%). Conclusions: This study represents the first application of machine learning techniques to predict healthcare personnel transition success in decentralization contexts. The SVM model effectively identified critical factors influencing workforce transitions, emphasizing the importance of balanced organizational support mechanisms. These findings provide evidence-based guidance for healthcare administrators implementing decentralization policies, offering generalizable insights for workforce management during health system reforms.