Heart failure prediction with machine learning: A comparative study

Authors

Jing Wang, University of Wollongong

Publication Name

Journal of Physics: Conference Series

Abstract

Heart failure is a worldwide healthy problem affecting more than 550,000 people every year. A better prediction for this disease is one of the key approaches of decreasing its impact. Both linear and machine learning models are used to predict heart failure based on various data as inputs, e.g., clinical features. In this paper, we give a comparative study of 18 popular machine learning models for heart failure prediction, with z-score or min-max normalization methods and Synthetic Minority Oversampling Technique (SMOTE) for the imbalance class problem which is often seen in this problem. Our results demonstrate the superiority of using z-score normalization and SMOTE for heart failure prediction.

Open Access Status

This publication may be available as open access

Volume

2031

Issue

1

Article Number

012068