Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction
This work addresses congestive heart failure prediction for medical diagnostics, but it appears incremental as it builds on existing extreme learning machine methods with new features.
The study tackled automated prediction of congestive heart failure from ECG signals by introducing a novel machine learning approach (R-HessELM) and feature models like inclined entropy, achieving an overall accuracy of 98.49%.
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.