Predicting Dental Anxiety and Cooperative Behavior in Children Using Machine Learning: A Cross-Sectional Predictive Modeling Study

TL;DR

This study uses machine learning to predict dental anxiety and cooperative behavior in children, identifying key factors and showing strong predictive accuracy.

Contribution

The study introduces a machine learning approach to predict dental anxiety and cooperation in children using clinical and behavioral data.

Findings

Logistic Regression models achieved 0.92 accuracy for dental anxiety and 0.91 for cooperative behavior prediction.

Random Forest Regressor predicted anxiety scores with an R2 of 0.97.

Sensory and cognitive responses were identified as key predictors of anxiety and cooperation.

Abstract

Background/Objectives: Dental anxiety and uncooperative behavior present significant challenges in pediatric dentistry and may adversely affect treatment outcomes and oral health. The main goal of this study was to evaluate the predictive performance of machine learning models in classifying dental anxiety measured using the Abeer Children Dental Anxiety Scale (ACDAS), predicting uncooperative behavior, estimating continuous dental anxiety scores, and identifying key predictors among children aged 6–11 years attending pediatric dental clinics in Jeddah, Saudi Arabia. Methods: This is an analytical cross-sectional study conducted among 952 children to evaluate whether machine learning models could predict dental anxiety and cooperative behavior based on demographic, clinical, and behavioral variables. Twenty variables captured demographic, medical, and dental history, BMI, and anxiety/behavioral measures. Data preprocessing included removing sparse variables, imputing missing values, and encoding categorical and ordinal variables appropriately. Logistic Regression models were trained to classify dental anxiety and cooperative behavior. A Random Forest Regressor was used to predict continuous anxiety scores, and a Random Forest Classifier was used for feature importance analysis. Principal Component Analysis (PCA) and K-Means clustering were applied to explore behavioral subgroups. Results: This dataset shows the Logistic Regression model with 0.92 accuracy (ROC AUC 0.98) for predicting dental anxiety and 0.91 accuracy (ROC AUC 0.95) for cooperative behavior. The Random Forest Regressor predicted anxiety scores with R2 = 0.97. Feature importance revealed that sensory and cognitive responses were key predictors of anxiety and cooperation. Unsupervised clustering identified two behavioral profiles: one with lower and another with higher anxiety and cooperation. Conclusions: ML models demonstrated strong prediction of dental anxiety and cooperation in this pediatric sample. While promising for early detection and personalized management of anxious or uncooperative children, further validation is essential before clinical use.

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