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Arrhythmia recognition based on integrated learning of multi-model fusion Stacking
1
School of Mathematics and Science, TaiZhou University, TaiZhou, China
2
School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing, China
3
School of Materials Science and Engineering, Northeastern University, Shenyang, Liaoning Province, China
4
Artificial Intelligence College, Shenyang Aerospace University, Liaoning Province, Shenyang, China
5
Business of College, Linyi University, Linyi, Shandong Province, China
*
Author to whom correspondence should be addressed.
JPHE 2024 7(1):244; https://doi.org/10.69610/j.phe.20240618
Received: 16 April 2024 / Accepted: 16 May 2024 / Published Online: 18 June 2024
Abstract
Current monitoring systems employing single model algorithms face challenges in accurately recognizing and alerting real-time arrhythmic events in patients with severe cardiac conditions. To address this issue and improve the detection accuracy of electrocardiogram (ECG) monitoring systems, this paper introduces a novel arrhythmia recognition model based on Stacking ensemble learning. This model integrates multiple base learners, including LightGBM, XGBoost, Random Forest, SVM, and Logistic Regression, and optimizes them using Gradient Boosting as the meta-learner. Hyperparameters were fine-tuned through grid search, and nested cross-validation was employed to train the model, ensuring robust predictive performance. The detection results indicate that the Stacking ensemble model significantly outperforms single models in both accuracy and stability, offering substantial practical application value for clinical ECG monitoring and diagnosis.
Keywords:
Arrhythmia，Multi-model fusion，Stacking ensemble learning，Grid search，Nested cross-validation;
Copyright: © 2024 by Ju, Yang, Liu, Gao, Li and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Ju, X.; Yang, K.; Liu, D.; Gao, H.; Li, H.; Zhang, Z. Arrhythmia recognition based on integrated learning of multi-model fusion Stacking. Journal of Public Health & Environment, 2024, 7, 244. doi:10.69610/j.phe.20240618
AMA Style
Ju X, Yang K, Liu D et al.. Arrhythmia recognition based on integrated learning of multi-model fusion Stacking. Journal of Public Health & Environment; 2024, 7(1):244. doi:10.69610/j.phe.20240618
Chicago/Turabian Style
Ju, Xiangchen; Yang, Kai; Liu, Dongxiong; Gao, Heyu; Li, Huiquan; Zhang, Zetong 2024. "Arrhythmia recognition based on integrated learning of multi-model fusion Stacking" Journal of Public Health & Environment 7, no.1:244. doi:10.69610/j.phe.20240618
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