Preoperatively predicting the pathological types of acute appendicitis using machine learning based on peripheral blood biomarkers and clinical features: a retrospective study.

ABSTRACT:

Background

This study aimed to establish machine learning models for preoperative prediction of the pathological types of acute appendicitis.

Methods

Based on histopathology, 136 patients with acute appendicitis were included and divided into three types: acute simple appendicitis (SA, n=8), acute purulent appendicitis (PA, n=104), and acute gangrenous or perforated appendicitis (GPA, n=24). Patients with SA/PA and PA/GPA were divided into training (70%) and testing (30%) sets. Statistically significant features (P<0.05) for pathology prediction were selected by univariate analysis. According to clinical and laboratory data, machine learning logistic regression (LR) models were built. Area under receiver operating characteristic curve (AUC) was used for model assessment.

SUBMITTER: Kang CB 

PROVIDER: S-EPMC8184413 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature