Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score.

ABSTRACT:

Objective

To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).

Research design and methods

Using data from 8,756 patients free at baseline of HF, with <10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).

Results

Over a median follow-up of 4.9 years, 319 patients (3.6%) developed incident HF. The RSF model

SUBMITTER: Segar MW 

PROVIDER: S-EPMC7364669 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature