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.
Abstract (translated)
这项研究探讨了使用机器学习（ML）模型将牙科提供者分类为两类——标准服务提供商和安全网诊所（SNC）提供者的方法。该研究基于2018年的数据集进行，包含24,300个实例和20个特征，其中包括高缺失值率（38.1%）。数据集中包含了各种服务计数（预防、治疗、检查）、交付系统（FFS、管理护理）以及受益人的人口统计信息。 为了识别关键预测因素，研究使用了诸如信息增益、Gini指数和ANOVA等特征排名方法。结果显示，与治疗相关的指标（如TXMT_USER_CNT和TXMT_SVC_CNT）是最重要的特征。 研究评估了12种机器学习模型，包括k-近邻算法（kNN）、决策树、支持向量机（SVM）、随机梯度下降（SGD）、随机森林、神经网络以及梯度提升等。这些模型通过十折交叉验证进行了测试，并在不同特征子集上评估了分类准确性。 实验结果表明，当使用所有20个特征时，神经网络实现了最高的准确率（94.1%），其次是梯度提升算法（93.2%）和随机森林（93.0%）。随着纳入更多特征，模型表现有所改善。随机梯度下降和支持向量机等方法在处理缺失数据方面表现出较强的鲁棒性。 此外，特征排名表明治疗服务计数以及注释代码是区分不同提供商类型的关键因素，而人口统计变量（如AGE_GROUP和CALENDAR_YEAR）影响较小。 该研究强调了特征选择对于提高模型效率和准确性的关键作用，特别是在不平衡的医疗保健数据集中。这些发现支持将特征排名技术与高级机器学习算法相结合以优化牙科提供者分类，并促进对服务不足人群进行针对性资源分配的方法。
URL
https://arxiv.org/abs/2506.04474
