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Background <p>Access to routine dental care is a cornerstone of preventive healthcare. Regular dental check-ups, which include professional cleanings, examinations, and preventive treatments, play a crucial role in preventing advanced dental diseases such as cavities, gum disease, and oral cancer. These check-ups help identify potential problems early, reducing the need for more invasive treatments and minimizing complications. This research aims to identify key determinants influencing patient behavior regarding dental care.</p> Methods <p>To identify influential factors affecting annual dental visits (ADV), we utilized the publicly available 2022 Behavioral Risk Factor Surveillance System (BRFSS) dataset, comprising survey records. This dataset captures health-related behaviors, chronic conditions, and access to preventive services among adults in the United States. We propose a hybrid method combining feature selection using the <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\mathcal {EAT}\)</EquationSource> </InlineEquation> transformer with machine learning (ML) models to uncover the determinants of ADV behavior.</p> Results <p>The proposed <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\mathcal {EAT}\)</EquationSource> </InlineEquation> model was evaluated using various transformer architectures. Among them, RoBERTa, ELECTRA, and BERT demonstrated the highest performance. Features selected by these top-performing models were subsequently used to train several ML models. CatBoost and XGBoost achieved the highest accuracies at 76.0% and 75.6%, respectively, while the decision tree achieved the lowest accuracy at 64.8%.</p> Conclusions <p>Our proposed method effectively reduced the feature space, thereby improving focus and reducing training and inference time without compromising accuracy. This fusion-based model provides valuable insights for healthcare providers, enabling the development of targeted interventions tailored to specific population needs. Understanding the factors contributing to irregular dental visits can guide evidence-based strategies to overcome barriers and improve overall oral health outcomes.</p>

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\(\mathcal {EAT}\) : explainable attentive transformers for identifying the factors influencing dental visits to enhance dental data completeness

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月17日

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  • Veena Mayya,
  • Giang T. Vu,
  • Babu Mandhidi,
  • Christian King,
  • Varadraj Gurupur,
  • Bert Little,
  • Astha Singhal

摘要

Background

Access to routine dental care is a cornerstone of preventive healthcare. Regular dental check-ups, which include professional cleanings, examinations, and preventive treatments, play a crucial role in preventing advanced dental diseases such as cavities, gum disease, and oral cancer. These check-ups help identify potential problems early, reducing the need for more invasive treatments and minimizing complications. This research aims to identify key determinants influencing patient behavior regarding dental care.

Methods

To identify influential factors affecting annual dental visits (ADV), we utilized the publicly available 2022 Behavioral Risk Factor Surveillance System (BRFSS) dataset, comprising survey records. This dataset captures health-related behaviors, chronic conditions, and access to preventive services among adults in the United States. We propose a hybrid method combining feature selection using the \(\mathcal {EAT}\) transformer with machine learning (ML) models to uncover the determinants of ADV behavior.

Results

The proposed \(\mathcal {EAT}\) model was evaluated using various transformer architectures. Among them, RoBERTa, ELECTRA, and BERT demonstrated the highest performance. Features selected by these top-performing models were subsequently used to train several ML models. CatBoost and XGBoost achieved the highest accuracies at 76.0% and 75.6%, respectively, while the decision tree achieved the lowest accuracy at 64.8%.

Conclusions

Our proposed method effectively reduced the feature space, thereby improving focus and reducing training and inference time without compromising accuracy. This fusion-based model provides valuable insights for healthcare providers, enabling the development of targeted interventions tailored to specific population needs. Understanding the factors contributing to irregular dental visits can guide evidence-based strategies to overcome barriers and improve overall oral health outcomes.