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

backgroundPaediatric cervical abscesses necessitate careful assessment to determine appropriate treatment strategies. Some patients require surgical intervention, although conservative management is effective. However, the criteria for the surgical indications remain unclear. Machine learning models have demonstrated promise in improving diagnostic accuracy across different medical fields.

objectiveThis study aimed to assess the use of machine learning models in predicting the requirement for surgical intervention in paediatric cervical abscesses and compare their performance with that of traditional logistic regression.

methodsA retrospective analysis was conducted on 55 paediatric patients diagnosed with cervical abscesses between 2010 and 2024. The patient demographics, clinical findings, laboratory data, and imaging characteristics were examined. Six predictive models were developed: logistic regression, Random Forest, Lasso regression, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine. Model performance was evaluated using the area under the curve (AUC), accuracy, precision, recall, and F1-score. Feature importance was examined to identify the main predictive factors.

resultsAmong all the factors, abscess size was the most significant predictor of surgical intervention. Machine-learning models, especially XGBoost, outperformed logistic regression, achieving the highest AUC, accuracy, and recall. Inflammatory markers, including neutrophil-to-lymphocyte ratio and neutrophil count, also substantially contributed to the prediction accuracy.

conclusionMachine learning models, particularly XGBoost, provide superior predictive performance compared with logistic regression, providing a valuable tool for optimising treatment decisions in paediatric cervical abscesses. These models improve clinical decision-making by integrating multiple factors, decreasing unnecessary surgeries, and enhancing patient outcomes.