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

2025-03-29

Data and Metadata

AI Summary

This study addresses the classification of dental service providers into standard institutions versus safety-net clinics—a critical task for optimizing healthcare resource allocation and strengthening policy support for vulnerable populations. Leveraging a large-scale, real-world 2018 claims dataset comprising 24,300 records—with 38.1% missing values and severe class imbalance—we conduct the first systematic performance evaluation of seven machine learning models: k-Nearest Neighbors, Support Vector Machine, Decision Tree, Stochastic Gradient Descent, Random Forest, Neural Network, and Gradient Boosting. Using 10-fold cross-validation and multi-metric assessment (AUC, F1-score, accuracy, recall), we find that Neural Networks (AUC = 0.975, accuracy = 94.1%) and Random Forest (AUC = 0.948, accuracy = 93.0%) significantly outperform traditional methods, demonstrating superior robustness to high missingness, distributional shift, and complex feature interactions. The results provide a reproducible ML framework and empirical foundation for accurately identifying safety-net providers and enabling targeted resource deployment.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Safety and Robustness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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. 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.

Problem

• Classifying dental providers using machine learning models
• Evaluating performance of ensemble models on imbalanced data
• Improving healthcare resource allocation through provider categorization

Innovation

• Neural Networks achieve highest AUC and accuracy
• Random Forest handles imbalanced data effectively
• Ensemble models outperform traditional classifiers significantly