Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: awaiting peer review]

Objective To develop predictive models for healthcare workforce transition success under decentralization using Support Vector Machine (SVM) analysis and identify key determinants across organizational support domains. Methods A cross-sectional study was conducted among healthcare personnel (n=430) who transferred from Ministry of Public Health facilities to Provincial Administrative Organizations in Thailand during 2023-2024. The SVM model evaluated 37 predictor variables spanning demographic characteristics, benefits, and welfare domains. Four kernel functions were compared to identify optimal model performance, and feature importance analysis was conducted to determine key predictors. Results The linear kernel demonstrated superior performance (accuracy: 71.43%, sensitivity: 49.02%, specificity: 85.37%) compared to other kernel functions. Analysis revealed five key predictors (feature weights >0.25): 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). While employee qualifications (0.236) emerged as a significant demographic predictor, organizational support factors, particularly financial incentives and professional development opportunities, showed stronger predictive power for transition success. 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.