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

TL;DR

This study compares various machine learning models, especially ensemble and neural networks, for classifying dental providers using fee-for-service data, demonstrating high accuracy and potential for improved healthcare resource allocation.

Contribution

It evaluates and identifies the superior performance of ensemble and neural network models in dental provider classification, highlighting their effectiveness over traditional methods.

Findings

Neural Networks achieved the highest AUC of 0.975 and accuracy of 94.1%.

Ensemble models outperformed traditional classifiers like Logistic Regression.

Models effectively handled imbalanced data and complex features.

Abstract

Dental provider classification plays a crucial role in optimizing healthcare resource allocation and policy planning. Effective categorization of providers, such as standard rendering providers and safety net clinic (SNC) providers, enhances service delivery to underserved populations. This study aimed to evaluate the performance of machine learning models in classifying dental providers using a 2018 dataset. A dataset of 24,300 instances with 20 features was analyzed, including beneficiary and service counts across fee-for-service (FFS), Geographic Managed Care, and Pre-Paid Health Plans. Providers were categorized by delivery system and patient age groups (0-20 and 21+). Despite 38.1% missing data, multiple machine learning algorithms were tested, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, and Gradient Boosting. A 10-fold cross-validation approach was applied, and models were evaluated using AUC, classification accuracy (CA), F1-score, precision, and recall. Neural Networks achieved the highest AUC (0.975) and CA (94.1%), followed by Random Forest (AUC: 0.948, CA: 93.0%). These models effectively handled imbalanced data and complex feature interactions, outperforming traditional classifiers like Logistic Regression and SVM. Advanced machine learning techniques, particularly ensemble and deep learning models, significantly enhance dental workforce classification. Their integration into healthcare analytics can improve provider identification and resource distribution, benefiting underserved populations.