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.
Abstract (translated)
牙科提供者分类在优化卫生资源分配和政策规划中起着关键作用。有效的提供者分类，如标准服务提供者和服务网诊所（SNC）提供者，增强了向边缘化人群提供的服务质量。本研究旨在评估机器学习模型在使用2018年数据集对牙科提供者进行分类时的性能表现。分析的数据集中包含了24,300个实例和20个特征，包括按费用报销（FFS）、地理管理医疗护理以及预付健康计划的服务受益人和服务数量统计。根据服务系统和患者年龄组（0-20岁及21岁以上）对提供者进行了分类。 尽管有高达38.1%的数据缺失，研究中测试了多种机器学习算法，包括k最近邻(kNN)、决策树、支持向量机(SVM)、随机梯度下降(SGD)、随机森林、神经网络和梯度提升。采用了一种十倍交叉验证的方法，并使用AUC（曲线下面积）、分类准确率(CA)、F1分数、精确度和召回率对模型进行评估。 在所有模型中，神经网络获得了最高的AUC值（0.975）和最高分类准确率（CA：94.1%），其次是随机森林（AUC: 0.948, CA: 93.0%）。这些模型能够有效处理不平衡数据及复杂的特征交互，优于传统的分类器如逻辑回归和支持向量机。先进的机器学习技术，尤其是集成和深度学习模型，在牙科工作力量的分类中表现出色。 将这些高级方法整合到医疗保健数据分析中，可以提高提供者的识别效率和服务资源分配的合理性，从而更好地服务于边缘化群体。
URL
https://arxiv.org/abs/2506.04479
