Sutradhar, Ananda and Al Rafi, Mustahsin and Shamrat, F. M. Javed Mehedi and Ghosh, Pronab and Das, Subrata and Islam, Md Anaytul and Ahmed, Kawsar and Zhou, Xujuan and Azad, A. K. M. and 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). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-023-48486-7.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | King Salman Centre for Disability Research [KSRG-2023-253] |
| Uncontrolled Keywords: | Anti-cancer peptides; Prediction;Classification |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Oct 2025 23:57 |
| Last Modified: | 21 Oct 2025 23:57 |
| URI: | http://eprints.um.edu.my/id/eprint/48071 |
Actions (login required)
 |
View Item |
