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Saqib M, Perswani P, Muneem A, Mumtaz H, Neha F, Ali S, Tabassum S. Machine learning in heart failure diagnosis, prediction, and prognosis: review. Ann Med Surg (Lond) 2024; 86:3615-3623. [PMID: 38846887 PMCID: PMC11152866 DOI: 10.1097/ms9.0000000000002138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024] Open
Abstract
Globally, cardiovascular diseases take the lives of over 17 million people each year, mostly through myocardial infarction, or MI, and heart failure (HF). This comprehensive literature review examines various aspects related to the diagnosis, prediction, and prognosis of HF in the context of machine learning (ML). The review covers an array of topics, including the diagnosis of HF with preserved ejection fraction (HFpEF) and the identification of high-risk patients with HF with reduced ejection fraction (HFrEF). The prediction of mortality in different HF populations using different ML approaches is explored, encompassing patients in the ICU, and HFpEF patients using biomarkers and gene expression. The review also delves into the prediction of mortality and hospitalization rates in HF patients with mid-range ejection fraction (HFmrEF) using ML methods. The findings highlight the significance of a multidimensional approach that encompasses clinical evaluation, laboratory assessments, and comprehensive research to improve our understanding and management of HF. Promising predictive models incorporating biomarkers, gene expression, and consideration of epigenetics demonstrate potential in estimating mortality and identifying high-risk HFpEF patients. This literature review serves as a valuable resource for researchers, clinicians, and healthcare professionals seeking a comprehensive and updated understanding of the role of ML diagnosis, prediction, and prognosis of HF across different subtypes and patient populations.
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Affiliation(s)
| | | | - Abraar Muneem
- College of Medicine, The Pennsylvania State University, Hershey, United States
| | | | - Fnu Neha
- Jinnah Sindh Medical University, Karachi
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Sutradhar A, Al Rafi M, Shamrat FMJM, Ghosh P, Das S, Islam MA, Ahmed K, Zhou X, Azad AKM, Alyami SA, Moni MA. BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients. Sci Rep 2023; 13:22874. [PMID: 38129433 PMCID: PMC10739972 DOI: 10.1038/s41598-023-48486-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
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Affiliation(s)
- Ananda Sutradhar
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Mustahsin Al Rafi
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - F M Javed Mehedi Shamrat
- Department of Computer System and Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Pronab Ghosh
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Subrata Das
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Md Anaytul Islam
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Kawsar Ahmed
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - A K M Azad
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Saudi Arabia
| | - Salem A Alyami
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Saudi Arabia
| | - Mohammad Ali Moni
- Centre for AI & Digital Health Technology, Artificial Intelligence & Cyber Future Institute, Charles Stuart University, Bathurst, NSW, 2795, Australia.
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