Survival Classification in Heart Failure Patients by Neural Network-Based Crocodile and Egyptian Plover (CEP) Optimization Algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-023-08183-z.pdf
Reference61 articles.
1. Rehman, A.; Khan, A.; Ali, M.A.; Khan, M.U.; Khan, S.U.; Ali, L.: Performance analysis of PCA, sparse PCA, kernel PCA and incremental PCA algorithms for heart failure prediction. 2nd Int Conf. Electr. Commun. Comput. Eng. ICECCE 2020(June), 1–5 (2020). https://doi.org/10.1109/ICECCE49384.2020.9179199
2. Karaoglu, A.N.; Caglar, H.; Degirmenci, A.; Karal, O.: Performance improvement with decision tree in predicting heart failure. In: Proc. 6th Int. Conf. Comput. Sci. Eng. UBMK 2021, pp. 781–784 (2021). https://doi.org/10.1109/UBMK52708.2021.9558939
3. Chicco, D.; Jurman, G.: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med. Inform. Decis. Mak. 20(16), 1–16 (2020). https://doi.org/10.1186/s12911-020-1023-5
4. Mehedi Zaman, S. M.; Qureshi, W. M.; Raihan, M. M. S.; Bin Shams, A.; Sultana, S.: Survival prediction of heart failure patients using stacked ensemble machine learning algorithm. In: Proc. 2021 IEEE Int. Women Eng. Conf. Electr. Comput. Eng. WIECON-ECE 2021, pp. 117–120, (2021). https://doi.org/10.1109/WIECON-ECE54711.2021.9829577
5. Liang, Y.; Guo, C.: Heart failure disease prediction and stratification with temporal electronic health records data using patient representation. Biocybern. Biomed. Eng. 43(1), 124–141 (2023). https://doi.org/10.1016/j.bbe.2022.12.008
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