{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/regularized-hesselm-and-inclined-entropy","title":"Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction","arxiv_id":"1907.05888","date":"2019-07-12","proceeding":null,"authors":["Apdullah Yayık","Yakup Kutlu","Gökhan Altan"],"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.","url_abs":"https://arxiv.org/abs/1907.05888v1","url_pdf":"https://arxiv.org/pdf/1907.05888v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/congestive-heart-failure-detection-on-chf","task":"Congestive Heart Failure detection","dataset":"CHF database","model":"Inclined Entropy (R-HessELM)","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"98.49","Precision":"98.05","Sensitivity":"98.3"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}