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

ABSTRACT:

Objective

This study aimed to develop a machine learning-based model to predict recurrence risk after perianal abscess surgery, thereby supporting personalized follow-up and intervention strategies.

Methods

Clinical data were collected from patients with perianal abscess who underwent surgery at Nanjing Hospital of Chinese Medicine, Affiliated to Nanjing University of Chinese Medicine between January 2022 and June 2023. Significant predictors were identified using the least absolute shrinkage and selection operator (LASSO) algorithm combined with multivariate logistic regression. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to balance class distribution, and several machine learning (ML) algorithms were employed for model construction. Model performance

SUBMITTER: Wang D 

PROVIDER: S-EPMC12700084 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature