Machine learning-based diagnostic prediction of minimal change disease: model development study.

ABSTRACT: Minimal change disease (MCD) is a common cause of nephrotic syndrome. Due to its rapid progression, early detection is essential; however, definitive diagnosis requires invasive kidney biopsy. This study aims to develop non-invasive predictive models for diagnosing MCD by machine learning. We retrospectively collected data on demographic characteristics, blood tests, and urine tests from patients with nephrotic syndrome who underwent kidney biopsy. We applied four machine learning algorithms-TabPFN, LightGBM, Random Forest, and Artificial Neural Network-and logistic regression. We compared their performance using stratified 5-repeated 5-fold cross-validation for the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Variabl

SUBMITTER: Noda R 

PROVIDER: S-EPMC11461711 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature