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- Publisher Website: 10.1016/j.cpcardiol.2022.101480
- Scopus: eid_2-s2.0-85145425390
- PMID: 36336116
- WOS: WOS:000918543600001
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Article: Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach
Title | Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach |
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Authors | |
Issue Date | 2023 |
Citation | Current Problems in Cardiology, 2023, v. 48, n. 2, article no. 101480 How to Cite? |
Abstract | Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS. A total of 1,028 patients with ACS admitted to Guangdong Second Provincial General Hospital between October 2019 and May 2022 were included in this study. 128 clinical features were ranked using Shapley additive exPlanations (SHAP) values and the top 20% of features were selected for building a balanced random forest (BRF) model. We compared the discriminatory capability of BRF with linear logistic regression (LLR). In the hold-out test set, the BRF model predicted subsequent HF with areas under the curve (AUC) of 0.76 (95% CI: 0.75-0.77), sensitivity of 0.97 (95% CI: 0.96-0.97), positive predictive value (PPV) of 0.73 (95% CI: 0.72-0.74), negative predictive value (NPV) of 0.63 (95% CI: 0.60-0.66), and accuracy of 0.73 (95% CI: 0.72-0.73), respectively. BRF outperforms linear logistic regression by 15.6% in AUC, 3.0% in sensitivity, and 60.8% in NPV. End-to-end machine learning approaches can predict the acute onset of HF following ACS with high prediction accuracy. This proof-of-concept study has the potential to substantially advance the management of ACS patients by utilizing the machine learning model as a triage tool to automatically identify clinically significant patients allowing for prioritization of interventions. |
Persistent Identifier | http://hdl.handle.net/10722/330893 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 0.934 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ren, Hao | - |
dc.contributor.author | Sun, Yu | - |
dc.contributor.author | Xu, Chenyu | - |
dc.contributor.author | Fang, Ming | - |
dc.contributor.author | Xu, Zhongzhi | - |
dc.contributor.author | Jing, Fengshi | - |
dc.contributor.author | Wang, Weilan | - |
dc.contributor.author | Tse, Gary | - |
dc.contributor.author | Zhang, Qingpeng | - |
dc.contributor.author | Cheng, Weibin | - |
dc.contributor.author | Jin, Wen | - |
dc.date.accessioned | 2023-09-05T12:15:39Z | - |
dc.date.available | 2023-09-05T12:15:39Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Current Problems in Cardiology, 2023, v. 48, n. 2, article no. 101480 | - |
dc.identifier.issn | 0146-2806 | - |
dc.identifier.uri | http://hdl.handle.net/10722/330893 | - |
dc.description.abstract | Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS. A total of 1,028 patients with ACS admitted to Guangdong Second Provincial General Hospital between October 2019 and May 2022 were included in this study. 128 clinical features were ranked using Shapley additive exPlanations (SHAP) values and the top 20% of features were selected for building a balanced random forest (BRF) model. We compared the discriminatory capability of BRF with linear logistic regression (LLR). In the hold-out test set, the BRF model predicted subsequent HF with areas under the curve (AUC) of 0.76 (95% CI: 0.75-0.77), sensitivity of 0.97 (95% CI: 0.96-0.97), positive predictive value (PPV) of 0.73 (95% CI: 0.72-0.74), negative predictive value (NPV) of 0.63 (95% CI: 0.60-0.66), and accuracy of 0.73 (95% CI: 0.72-0.73), respectively. BRF outperforms linear logistic regression by 15.6% in AUC, 3.0% in sensitivity, and 60.8% in NPV. End-to-end machine learning approaches can predict the acute onset of HF following ACS with high prediction accuracy. This proof-of-concept study has the potential to substantially advance the management of ACS patients by utilizing the machine learning model as a triage tool to automatically identify clinically significant patients allowing for prioritization of interventions. | - |
dc.language | eng | - |
dc.relation.ispartof | Current Problems in Cardiology | - |
dc.title | Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.cpcardiol.2022.101480 | - |
dc.identifier.pmid | 36336116 | - |
dc.identifier.scopus | eid_2-s2.0-85145425390 | - |
dc.identifier.volume | 48 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | article no. 101480 | - |
dc.identifier.epage | article no. 101480 | - |
dc.identifier.eissn | 1535-6280 | - |
dc.identifier.isi | WOS:000918543600001 | - |