Machine learning based congestive heart failure detection using feature importance ranking of multimodal features

DOI10.3934/mbe.2021004zbMath1471.92178OpenAlexW3104401935MaRDI QIDQ1980031

Jalal S. Alowibdi, Ishtiaq Rasool Khan, Lal Hussain, Wajid Aziz, Monagi H. Alkinani

Publication date: 3 September 2021

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2021004

zbMATH Keywords

decision treemachine learningsupport vector machinecongestive heart failurenormal sinus rhythmempirical receiver operating characteristicsmultimodal features

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biomedical imaging and signal processing (92C55)

Uses Software

• PhysioToolkit

Cites Work

• Local Shannon entropy measure with statistical tests for image randomness
• Bayesian reliability models of Weibull systems: State of the art
• Approximate entropy as a measure of system complexity.
• Fisher information and stochastic complexity

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