{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:44:09Z","timestamp":1787013849125,"version":"build-2736575974"},"reference-count":26,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Noninvasive Electrocardiol"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Heart failure (HF) is a major driver of global morbidity and mortality. Early identification of patients at risk remains challenging due to complex, multivariate clinical relationships. Machine learning (ML) methods offer promise for more accurate prognostication.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We evaluated the predictive value of electrocardiogram (ECG)\u2013derived features and developed an ML model to stratify HF risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We analyzed a public cohort of 1061 patients, of whom 589 (55.5%) developed HF. Records were randomly divided into training (70%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009742) and test (30%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009319) sets. After preprocessing, we trained a random forest (RF) classifier. Performance on the test set was assessed via accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). Feature selection employed Gini importance and the Boruta algorithm, while SHAP values provided model interpretability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The RF model achieved an AUC of 0.969, with 91.8% accuracy, 93.8% sensitivity, 89.4% specificity, and a 92.7% F1\u2010score. The top predictors included ST depression (Oldpeak), maximum heart rate (MaxHR), ST\u2010segment slope, and serum cholesterol. Confusion matrix analysis confirmed robust discrimination between HF and non\u2010HF cases. SHAP interpretation reinforced the dominant influence of ECG\u2010related indices and cholesterol on individual risk estimates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An RF model leveraging ECG features demonstrated excellent performance for HF risk prediction and highlighted key physiologic markers. Future work should integrate comorbidity profiles and detailed biochemical data to further enhance clinical applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/anec.70097","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T22:38:51Z","timestamp":1751063931000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predictive Modeling of Heart Failure Outcomes Using\n                    <scp>ECG<\/scp>\n                    Monitoring Indicators and Machine Learning"],"prefix":"10.1111","volume":"30","author":[{"given":"Jia","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Cardiology, Zone 6 The First People's Hospital of Chenzhou  Chenzhou HuNan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Zone 6 The First People's Hospital of Chenzhou  Chenzhou HuNan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingzhi","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Zone 6 The First People's Hospital of Chenzhou  Chenzhou HuNan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3776-7140","authenticated-orcid":false,"given":"Xiaoliang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Zone 6 The First People's Hospital of Chenzhou  Chenzhou HuNan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacasi.2023.05.010"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/a16020088"},{"issue":"8","key":"e_1_2_12_4_1","doi-asserted-by":"crossref","DOI":"10.36660\/abc.20230771","article-title":"An\u00e1lise Comparativa do ECG Com o Holter na Avalia\u00e7\u00e3o da Frequ\u00eancia Card\u00edaca na Insufici\u00eancia Card\u00edaca Com Fra\u00e7\u00e3o de Eje\u00e7\u00e3o Reduzida e Ritmo Sinusal","volume":"121","author":"Camazzola F. E.","year":"2024","journal-title":"Arquivos Brasileiros de Cardiologia"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/medicina59071314"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehl259"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehn309"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurjhf\/hft129"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.eurheartj.a059455"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.14740\/cr473w"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32279-7"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2011.03.072"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/europace\/eux032"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1053\/euhj.2001.2774"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.2196\/38082"},{"key":"e_1_2_12_16_1","unstructured":"Lundberg S. M. andS. I.Lee.2017.\u201cA Unified Approach to Interpreting Model Predictions.\u201dNIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems; NIPS'17; Dec 4\u20139; Long Beach CA. pp. 4768\u20134777."},{"key":"e_1_2_12_17_1","unstructured":"Rahulraj P. V. D. K.n.d.\u201cHeart Failure Prediction Dataset.\u201dKaggle.https:\/\/www.kaggle.com\/datasets\/rahulrajpvduk\/heart\u2010failure\u2010prediction\u2010dataset."},{"key":"e_1_2_12_18_1","first-page":"132","article-title":"Predictive Analysis of Heart Diseases With Machine Learning Approaches","volume":"2022","author":"Ramesh T. R.","year":"2022","journal-title":"Malaysian Journal of Computer Science"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-45925-3"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelectrocard.2019.12.010"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/biom13071153"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1532-5415.2005.00492.x"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11739-012-0862-1"},{"issue":"8","key":"e_1_2_12_24_1","article-title":"Resting Heart Rate to Assess Patients With Heart Failure: That Is All We Need","volume":"121","author":"Villacorta H.","year":"2024","journal-title":"Frequ\u00eancia Card\u00edaca de Repouso Para Avaliar Pacientes Com Insufici\u00eancia Card\u00edaca: \u00c9 Tudo o Que Precisamos. Arquivos Brasileiros de Cardiologia"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1097\/HCO.0000000000000266"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.123.068576"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelectrocard.2021.08.005"}],"container-title":["Annals of Noninvasive Electrocardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/anec.70097","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T05:08:15Z","timestamp":1753679295000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/anec.70097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,27]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["10.1111\/anec.70097"],"URL":"https:\/\/doi.org\/10.1111\/anec.70097","archive":["Portico"],"relation":{},"ISSN":["1082-720X","1542-474X"],"issn-type":[{"value":"1082-720X","type":"print"},{"value":"1542-474X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,27]]},"assertion":[{"value":"2025-03-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70097"}}