Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients.

ABSTRACT:

Introduction

The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.

Methods

We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared.

Results

Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF r

SUBMITTER: Xu L 

PROVIDER: S-EPMC10946267 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature