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Background: While prior studies have identified a number of demographic factors related to general health practitioners’ decision to stay in public health practice, recruitment agencies have no validated methods to predict how long these health workers will commit to their placement. We aim to use machine learning methods to predict health professional’s length of practice in the rural public healthcare sector. Methods: Recruitment and retention data from Africa Health Placements (n=13 698 with 1 838 completers) was used to development machine learning models to predict health workers’ length of practice. A cross-validation technique was used to validate the models, to evaluate which model performs better, based on their respective aggregated error rate of prediction. Length of stay was categorised into 4 groups (less than 1 year, less than 2 years, less than 3 years, and more than 3 years). Three machine learning models were trained and used 10-fold cross validation techniques to attain evaluative statistics. Results: The three models attain almost identical results, with negligible difference in accuracy. The ‘best’-performing model (Multinomial logistic classifier) achieved a 47.34% [SD 1.63] while the decision tree model achieved an almost comparable 45.82% [SD 1.69]. The three models achieved the average AUC of approximately 0.66 suggesting sufficient predictive signal at the four categorical variables selected. Conclusions: Machine learning models give us an effective tool to predict the recruited health workers’ length of practice. These models can be adapted beyond the scope of demographic information such as information about placement location and income. This modelling will also, allow strategic planning and optimization of public health care recruitment. Key message Human resource planning in healthcare can employ machine learning to effectively predict length of stay of recruited health workers who are stationed rural areas.

Predicting the Length of Stay among Healthcare Workers in Underserved Communities: A Quantitative Retrospective Cohort Study