Identifying Risk and Protective Factors for Attrition Among Recently Enlisted Navy Personnel Using Variable Importance Measures

Five groups of variables were found to be predictive of Navy attrition: Mental health (MH) disorder, alcohol-related problems, occupations, sex, medical appointment compliance, and pain management. These results demonstrate the utility of machine learning models in predicting attrition compared with regression analyses. VIM is a valuable tool that could be used in decision-making processes in the context of military personnel management and retention. Furthermore, ensemble approaches, compared with a single decision tree, improve overall predictive performance and result in a more robust model that is resistant to overfitting.