Papers › Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction
Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction
Apdullah Yayık, Yakup Kutlu, Gökhan Altan
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Congestive Heart Failure detection | CHF database | Inclined Entropy (R-HessELM) | Accuracy | 98.49 | #1 of 1 | Archive leaderboard | report |
| Congestive Heart Failure detection | CHF database | Inclined Entropy (R-HessELM) | Precision | 98.05 | #1 of 1 | Archive leaderboard | report |
| Congestive Heart Failure detection | CHF database | Inclined Entropy (R-HessELM) | Sensitivity | 98.3 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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