Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death.

ABSTRACT:

Background

The prediction of readmission or death after a hospital discharge for heart failure (HF) remains a major challenge. Modern healthcare systems, electronic health records, and machine learning (ML) techniques allow us to mine data to select the most significant variables (allowing for reduction in the number of variables) without compromising the performance of models used for prediction of readmission and death. Moreover, ML methods based on transformation of variables may potentially further improve the performance.

Objective

To use ML techniques to determine the most relevant and also transform variables for the prediction of 30-day readmission or death in HF patients.

Methods

We identified all Western Australian patients aged 65 years and above admitted f

SUBMITTER: Awan SE 

PROVIDER: S-EPMC6594617 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature