Machine learning-based prediction model for chronic brucellosis: a multi-feature approach using clinical and laboratory data.

ABSTRACT:

Background

Chronic progression is a major clinical challenge in human brucellosis (HB), affecting nearly one-third of patients and leading to long-term disability. Reliable early prediction tools are lacking, hindering timely risk stratification and individualized management. This study aimed to develop and validate machine learning (ML) models to predict chronic progression using routinely available clinical and laboratory data.

Methods

We retrospectively analyzed 555 patients with confirmed brucellosis admitted between 2019 and 2024. Clinical characteristics and laboratory indicators at admission were collected. Feature selection was performed using Boruta and recursive feature elimination. Six supervised ML models (random forest [RF], LightGBM, XGBoost, logistic regressio

SUBMITTER: Wang R 

PROVIDER: S-EPMC12672903 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature