Machine learning-based prediction models for renal impairment in Chinese adults with hyperuricaemia: risk factor analysis.

ABSTRACT: In hyperuricaemic populations, multiple factors may contribute to impaired renal function. This study aimed to establish a machine learning-based model to identify characteristic factors related to renal impairment in hyperuricaemic patients, determine dose‒response relationships, and facilitate early intervention strategies. Data were collected through the big data platform of Nanjing Hospital of Traditional Chinese Medicine, encompassing 2,705 patients with hyperuricaemia (1,577 with renal impairment, 828 without) from June 2019 to June 2022. After multiple imputations for missing values, the dataset was randomly split into training (70%) and validation (30%) sets. We employed three machine learning algorithms for feature selection: random forest (with 100 decision trees and an OOB error

SUBMITTER: Wu T 

PROVIDER: S-EPMC11910588 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature