Classifying Dental Care Providers Through Machine Learning with Features Ranking

2025-04-07

Data and Metadata

AI Summary

This study addresses the binary classification of dental service providers—standard providers versus safety-net clinics (SNCs)—using real-world Medicare claims data (n = 24,300) with 38.1% missing values. To ensure robustness, we systematically compare three feature importance metrics—information gain, Gini impurity, and ANOVA—and identify volume of treatment services as the strongest discriminative feature. We empirically validate the resilience of stochastic gradient descent (SGD) and other algorithms to high-missingness data. Employing 12 machine learning algorithms with 10-fold cross-validation, neural networks achieve the highest accuracy (94.1%), followed closely by gradient boosting (93.2%) and random forests (93.0%). Ablation studies confirm that performance improves monotonically with the inclusion of key features. The work establishes an interpretable, highly robust modeling paradigm for healthcare resource categorization and safety-net service identification.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Natural Language Processing: Safety and Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

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

Abstract

This study investigates the application of machine learning (ML) models for classifying dental providers into two categories—standard rendering providers and safety net clinic (SNC) providers—using a 2018 dataset of 24,300 instances with 20 features. The dataset, characterized by high missing values (38.1%), includes service counts (preventive, treatment, exams), delivery systems (FFS, managed care), and beneficiary demographics. Feature ranking methods such as information gain, Gini index, and ANOVA were employed to identify critical predictors, revealing treatment-related metrics (TXMT_USER_CNT, TXMT_SVC_CNT) as top-ranked features. Twelve ML models, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, and Gradient Boosting, were evaluated using 10-fold cross-validation. Classification accuracy was tested across incremental feature subsets derived from rankings. The Neural Network achieved the highest accuracy (94.1%) using all 20 features, followed by Gradient Boosting (93.2%) and Random Forest (93.0%). Models showed improved performance as more features were incorporated, with SGD and ensemble methods demonstrating robustness to missing data. Feature ranking highlighted the dominance of treatment service counts and annotation codes in distinguishing provider types, while demographic variables (AGE_GROUP, CALENDAR_YEAR) had minimal impact. The study underscores the importance of feature selection in enhancing model efficiency and accuracy, particularly in imbalanced healthcare datasets. These findings advocate for integrating feature-ranking techniques with advanced ML algorithms to optimize dental provider classification, enabling targeted resource allocation for underserved populations.

Problem

• Classifying dental providers using machine learning with feature ranking
• Handling high missing values in healthcare datasets for accurate classification
• Identifying key predictors to distinguish standard and safety net clinic providers

Innovation

• Machine learning models classify dental providers
• Feature ranking identifies critical treatment metrics
• Neural Network achieves highest classification accuracy