Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling.

ABSTRACT:

Purpose

The distinction between complicated appendicitis (CAP) and uncomplicated appendicitis (UAP) remains challenging. The purpose of this study was to construct a safe and economical diagnostic model that can accurately and rapidly differentiate between CAP and UAP.

Methods

Patient data from 773 appendectomies were retrospectively collected, important features were selected using random forests, and the data were divided into training and test sets in a 3:1 ratio. An integrated learning algorithm, Extreme Gradient Boosting (XGBoost), was introduced to predict the risk of CAP and compared with Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (CART) algorithms. A comprehensive comparison of the four algorithms was performed using model performance metrics

SUBMITTER: Chen S 

PROVIDER: S-EPMC12023617 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature