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Pediatric Maxillofacial Trauma: Machine Learning Based Predictive Modeling to Identify Trauma Patterns.

Elsy Antony¹, Saima Yunus Khan¹, Md Kalim Ansari²

¹Department of Pediatric and Preventive Dentistry, Dr. Ziauddin Ahmad Dental College, Aligarh Muslim University, Aligarh, India.

Dental Traumatology : Official Publication of International Association for Dental Traumatology

|June 13, 2025

Summary

This summary is machine-generated.

Machine learning models accurately predict pediatric facial trauma, identifying age and socioeconomic factors like parental education and employment as key predictors. Children from lower socioeconomic backgrounds face a higher risk of such injuries.

Keywords:

Bayesian Network machine learning algorithms pediatric maxillofacial trauma traumatic dental injury

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Area of Science:

Medical Informatics
Pediatric Traumatology
Machine Learning in Healthcare

Background:

Pediatric facial trauma presents unique challenges in diagnosis and management.
Predictive modeling can identify at-risk populations and inform preventative strategies.

Purpose of the Study:

To analyze characteristics of pediatric facial trauma.
To predict influencing factors using machine learning algorithms.

Main Methods:

A prospective hospital-based study included pediatric patients (up to 15 years) with maxillofacial trauma.
Data analyzed using logistic regression, Bayesian Network, CHAID, and Neural Network algorithms.

Main Results:

Bayesian Network and Logistic Regression achieved 92.59% accuracy.
Age, paternal education, maternal education, and parental employment were significant predictors.
Bayesian Network identified Road Traffic Accidents (RTA) and sex as other key variables.

Conclusions:

Age is the most crucial factor in predicting pediatric maxillofacial trauma.
Parental education and employment are significant predictors, indicating higher risk in lower socioeconomic groups.
Machine learning models offer high accuracy in identifying risk factors for pediatric facial trauma.