Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.

ABSTRACT:

Background

Classification of the etiologies of pleural effusion is a critical challenge in clinical practice. Traditional diagnostic methods rely on a simple cut-off method based on the laboratory tests. However, machine learning (ML) offers a novel approach based on artificial intelligence to improving diagnostic accuracy and capture the non-linear relationships.

Method

A retrospective study was conducted using data from patients diagnosed with pleural effusion. The dataset was divided into training and test set with a ratio of 7:3 with 6 machine learning algorithms implemented to diagnosis pleural effusion. Model performances were assessed by accuracy, precision, recall, F1 scores and area under the receiver operating characteristic curve (AUC). Feature importance and aver

SUBMITTER: Chen QY 

PROVIDER: S-EPMC12048966 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature