BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients
Article
Sutradhar, Ananda, Al Rafi, Mustahsin, Shamrat, F M Javed Mehedi, Ghosh, Pronab, Das, Subrata, Islam, MdAnaytul, Ahmed, Kawsar, Zhou, Xujuan, Azad, A. K. M, Alyami, Salem A. and Moni, Mohammad Ali. 2023. "BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients." Scientific Reports. 13 (1). https://doi.org/10.1038/s41598-023-48486-7
Article Title | BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients |
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ERA Journal ID | 201487 |
Article Category | Article |
Authors | Sutradhar, Ananda, Al Rafi, Mustahsin, Shamrat, F M Javed Mehedi, Ghosh, Pronab, Das, Subrata, Islam, MdAnaytul, Ahmed, Kawsar, Zhou, Xujuan, Azad, A. K. M, Alyami, Salem A. and Moni, Mohammad Ali |
Journal Title | Scientific Reports |
Journal Citation | 13 (1) |
Article Number | 22874 |
Number of Pages | 16 |
Year | 2023 |
Publisher | Nature Publishing Group |
Place of Publication | United States |
ISSN | 2045-2322 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-023-48486-7 |
Web Address (URL) | https://www.nature.com/articles/s41598-023-48486-7 |
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. © 2023, The Author(s). |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
420699. Public health not elsewhere classified | |
Byline Affiliations | Daffodil International University, Bangladesh |
University of Malaya, Malaysia | |
Lakehead University, Canada | |
University of Saskatchewan, Canada | |
Mawlana Bhashani Science and Technology University, Bangladesh | |
School of Business | |
Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia | |
Charles Sturt University |
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