Machine learning-based prediction of gastroparesis risk following complete mesocolic excision.

ABSTRACT:

Background

Gastroparesis is a major complication following complete mesocolic excision (CME) and significantly impacts patient outcomes. This study aimed to create a machine learning model to pinpoint key risk factors before, during, and after surgery, effectively predicting the risk of gastroparesis after CME.

Methods

The study involved 1146 patients with colon cancer, out of which 95 developed gastroparesis. Data were collected on 34 variables, including demographics, chronic conditions, pre-surgery test results, types of surgery, and intraoperative details. Four machine learning techniques were employed: extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). The evaluation involved k-fold cross-validation, rece

SUBMITTER: Wang W 

PROVIDER: S-EPMC11436699 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature