Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction

TL;DR

This paper introduces a novel machine learning method called R-HessELM combined with inclined entropy features to accurately predict congestive heart failure from ECG signals, achieving 98.49% accuracy.

Contribution

The study presents a new R-HessELM algorithm and demonstrates the effectiveness of inclined entropy features for CHF prediction from ECG data.

Findings

Achieved 98.49% overall accuracy in CHF prediction.

Inclined entropy features effectively represent ECG signal characteristics.

R-HessELM outperforms existing methods in this application.

Abstract

Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models; squared, circled, inclined and grid entropy measurement were introduced and used for prediction of CHF. This study proved that inclined entropy measurements features well represent characteristics of ECG signals and together with R-HessELM approach overall accuracy of 98.49% was achieved.