Predicting Incident Heart Failure in Women With Machine Learning: The Women's Health Initiative Cohort.

ABSTRACT:

Background

Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women's Health Initiative (WHI).

Methods

We used 2 machine-learning methods-Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)-to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atheroscl

SUBMITTER: Tison GH 

PROVIDER: S-EPMC8642266 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature