The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.

ABSTRACT:

Introduction

Malignancies of the GI tract account for one-third of cancer-related deaths globally and more than 25% of all cancer diagnoses. The rising prevalence of GI tract malignancies and the shortcomings of existing treatment approaches highlight the need for better predictive prediction models. RF's machine-learning method can predict cancers by using numerous decision trees to locate, classify, and forecast data. This systematic study aims to assess how well RF models predict the prognosis of GI tract malignancies.

Methods

Following PRISMA criteria, we performed a systematic search in PubMed, Scopus, Google Scholar, and Web of Science until May 28, 2024. Studies used RF models to forecast the prognosis of GI tract malignancies, including esophageal, gastric, and color

SUBMITTER: Mohamadi Z 

PROVIDER: S-EPMC12315591 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature