Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.

ABSTRACT:

Background

Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.

Objective

To develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.

Methods

Retrospective analysis was conducted on 606 adult sepsis inpatients at our medical center between January 2020 and December 2022, who were randomly divided into training and validation sets in a 7:3 ratio. Traditional logistic regression and machine learning

SUBMITTER: Wu H 

PROVIDER: S-EPMC11743956 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature