Classifying Dental Care Providers Through Machine Learning with Features Ranking

• URL: http://arxiv.org/abs/2506.04474v1
• Date: Wed, 04 Jun 2025 21:45:40 GMT
• Title: Classifying Dental Care Providers Through Machine Learning with Features Ranking
• Authors: Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mohammed Hasan Abu-Arqoub, Rashiq Rafiq Marie,
• Abstract summary: This study investigates the application of machine learning (ML) models for classifying dental providers.<n>The dataset includes service counts (preventive, treatment, exams), delivery systems (FFS, managed care), and beneficiary demographics.<n>The study underscores the importance of feature selection in enhancing model efficiency and accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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