# Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction Source: [Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:06:37.356Z Authors: Apdullah Yay{\\i}k, Yakup Kutlu, G\\"okhan Altan Published: 2019-07-12T11:11:02.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Congestive Heart Failure Identification on Normal vs Congestive Heart Failure (CHF)](): Congestive Heart Failure Identification; Normal vs Congestive Heart Failure (CHF); Overall Accuracy: 98.41. - [ECG Arrhythmia Recognition on Normal, CHF, Ventricular Arrhythmia, and Atrial Fibrillation](): ECG Arrhythmia Recognition; Normal, CHF, Ventricular Arrhythmia, and Atrial Fibrillation; Overall Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()