A systematic mapping study for ensemble classification methods in cardiovascular disease
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Linguistics and Language,Language and Linguistics
Link
https://link.springer.com/content/pdf/10.1007/s10462-020-09914-6.pdf
Reference128 articles.
1. Abawajy J, Kelarev A, Chowdhury MU, Herbert FJ (2016) Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles. IEEE J Biomed Heal Inform 20(1):408–415. https://doi.org/10.1109/JBHI.2014.2363177
2. Ahmad T et al (2018) Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. J Am Heart Assoc 7(8):1–15. https://doi.org/10.1161/JAHA.117.008081
3. Ahmed H, Younis EMG, Hendawi A, Ali AA (2019) Heart disease identification from patients’ social posts, machine learning solution on Spark. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2019.09.056
4. Aksela M, Laaksonen J (2006) Using diversity of errors for selecting members of a committee classifier. Pattern Recognit 39(4):608–623. https://doi.org/10.1016/j.patcog.2005.08.017
5. Alizadehsani R et al (2013) A data mining approach for diagnosis of coronary artery disease. Comput Methods Programs Biomed 111(1):52–61. https://doi.org/10.1016/j.cmpb.2013.03.004
Cited by 17 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Multi-Class Imbalanced Data Classification: A Systematic Mapping Study;Engineering, Technology & Applied Science Research;2024-06-01
2. Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning;American Heart Journal;2024-05
3. Automatic Classification of Cardiac Arrhythmias Using Deep Learning Techniques: A Systematic Review;IEEE Access;2024
4. Decision Support System Based on Machine Learning Techniques to Diagnosis Heart Disease Using Four-Lead ECG Recordings;Lecture Notes in Networks and Systems;2024
5. Performance analysis of machine learning algorithms: Single Model VS Ensemble Model;Journal of Physics: Conference Series;2023-10-01
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3