Machine learning model matters its accuracy: a comparative study of ensemble learning and AutoML using heart disease prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16380-z.pdf
Reference31 articles.
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2. Akrivopoulos O, Amaxilatis D, Antoniou A, Chatzigiannakis I (2017) Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure. In: Proceedings of the first international workshop on human-centered sensing, networking, and systems, pp 25–30. https://doi.org/10.1145/3144730.3144736
3. Ali F et al (2020) A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf Fusion 63:208–222. https://doi.org/10.1016/j.inffus.2020.06.008
4. Banerjee M, Reynolds E, Andersson HB, Nallamothu BK (2019) Tree-based analysis: a practical approach to create clinical decision-making tools. Circ Cardiovasc Qual Outcomes 12(5):e004879. https://doi.org/10.1161/CIRCOUTCOMES.118.004879
5. Belkin M, Hsu D, Ma S, Mandal S (2019) Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proc Natl Acad Sci 116(32):15849–15854. https://doi.org/10.1073/pnas.1903070116
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