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Postoperative recurrence prediction model for perianal abscess using machine learning algorithms.

Dawei Wang¹, Caixia Zhang¹, Zhiran Li²

¹National Colorectal Disease Center, Nanjing Hospital of Chinese Medicine, Affiliated to Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.

Frontiers in Public Health

|December 15, 2025

Summary

This summary is machine-generated.

A machine learning model predicts perianal abscess recurrence risk. The CatBoost model, utilizing diabetes history, abscess space, and AISI, offers personalized patient management after surgery.

Keywords:

CatBoost Shap machine learning perianal abscess recurrence

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Area of Science:

Medical Informatics
Surgical Oncology
Machine Learning in Healthcare

Background:

Perianal abscess recurrence poses a significant challenge, necessitating improved risk stratification.
Personalized follow-up strategies are crucial for optimizing patient outcomes post-surgery.

Purpose of the Study:

To develop and validate a machine learning (ML) model for predicting recurrence risk in patients undergoing perianal abscess surgery.
To identify key clinical predictors contributing to post-operative recurrence.

Main Methods:

Clinical data from 737 patients were analyzed.
LASSO regression and multivariate logistic regression identified significant predictors.
SMOTE balanced the dataset; ML algorithms including CatBoost were employed.
Model performance was assessed using AUC, sensitivity, specificity, accuracy, calibration curves, DCA, and SHAP for interpretability.

Main Results:

Diabetes history, abscess space, and Aggregate Index of Systemic Inflammation (AISI) were identified as strong recurrence predictors.
The CatBoost model demonstrated superior predictive performance across training, validation, and temporal validation sets (AUCs ranging from 0.735 to 0.821).

Conclusions:

The developed ML model, particularly the CatBoost algorithm, effectively predicts perianal abscess recurrence risk.
SHAP analysis provides interpretability, facilitating individualized patient management and targeted interventions.