Analysis of Machine Learning Techniques for Heart Failure Readmissions.

ABSTRACT:

Background

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions.

Methods and results

Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patie

SUBMITTER: Mortazavi BJ 

PROVIDER: S-EPMC5459389 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature