A new sample reduction method for decreasing the running time of the k-nearest neighbors algorithm to diagnose patients with congestive heart failure: backward iterative elimination
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s12046-023-02105-3.pdf
Reference55 articles.
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3. Sayilgan E, Cura O K and Isler Y 2017 Use of clustering algorithms and extreme learning machine in determining arrhythmia types. In: 2017 25th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. IEEE, Antalya
4. Ponikowski P, Voors A A, Anker S D, Bueno H, Cleland J G, Coats A J, Falk V, González-Juanatey J R, Harjola V P, Jankowska E A and Jessup M 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur. J. Heart Fail. 18(8): 891–975
5. Eltrass A S, Tayel M B and Ammar A I 2021 A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform. Biomed. Signal Proces. Control 65: 102326
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