<ul><li>Machine learning models were evaluated for classifying dental providers using a 2018 dataset of 24,300 instances with 20 features.</li><li>Neural Networks achieved the highest AUC (0.975) and classification accuracy (94.1%) in classifying dental providers, followed by Random Forest (AUC: 0.948, CA: 93.0%).</li><li>Despite 38.1% missing data, advanced machine learning techniques, particularly ensemble and deep learning models, outperformed traditional classifiers like Logistic Regression and SVM.</li><li>Integration of these advanced machine learning models in healthcare analytics can enhance dental provider identification and resource distribution, especially benefiting underserved populations.</li></ul>

Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data

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