Abstract
This is the first known study to look at the outcome of low-value utilization, targeting individuals who are underutilizing outpatient services. The random forest model performed better than the decision tree; however, features were similar in both models, with area deprivation index as the key variable in predicting low-value utilization. The decision tree was able to produce specific cutoff points, making it more interpretable and useful for clinical application. Both models can be used to create clinical tools for identifying and targeting individuals for intervention and follow-up.