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Predicting surgical intervention in paediatric cervical abscesses using machine learning: a comparative analysis.

Ryota Koshu¹, Masao Noda², Haruna Nakamoto¹

¹Department of Otolaryngology and Head and Neck Surgery, Jichi Medical University, Shimotsuke, Japan.

European Archives of Oto-Rhino-Laryngology : Official Journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

|June 3, 2025

Summary

This summary is machine-generated.

Machine learning models, particularly XGBoost, can predict the need for surgery in pediatric cervical abscesses better than traditional methods. This aids in optimizing treatment and improving patient outcomes.

Keywords:

Machine learning models Paediatric cervical abscesses Paediatric otolaryngology Predictive models Surgical intervention

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Area of Science:

Medical Informatics
Machine Learning in Medicine
Pediatric Surgery

Background:

Paediatric cervical abscesses require careful assessment for treatment, with surgical intervention sometimes necessary but indications unclear.
Machine learning (ML) shows potential for enhancing diagnostic accuracy in various medical fields.

Purpose of the Study:

To evaluate ML models for predicting surgical intervention in paediatric cervical abscesses.
To compare ML model performance against traditional logistic regression.

Main Methods:

Retrospective analysis of 55 paediatric patients with cervical abscesses (2010-2024).
Development and comparison of six predictive models: logistic regression, Random Forest, Lasso, SVM, XGBoost, and LightGBM.
Performance evaluation using AUC, accuracy, precision, recall, and F1-score.

Main Results:

Abscess size was the most significant predictor of surgical need.
XGBoost demonstrated superior performance over logistic regression, with higher AUC, accuracy, and recall.
Inflammatory markers like neutrophil-to-lymphocyte ratio and neutrophil count were key predictive factors.

Conclusions:

ML models, especially XGBoost, offer superior predictive capabilities for paediatric cervical abscesses compared to logistic regression.
These models enhance clinical decision-making, reduce unnecessary surgeries, and improve patient outcomes.