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Title: A Framework Design for Heart Failure Detection: Analyzes on Features and Hybrid Classifiers
Authors: Koyuncu, Hasan
Keywords: chaotic
framework design
gauss map
heart failure
optimized classifier
pattern classification
Classification (of information)
Feature extraction
Failure detection
Feature classifiers
Framework designs
Gauss maps
Heart failure
Hybrid classifier
Optimized classifier
Patterns classification
Scan techniques
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: The detection of heart failure is a vital and complicated issue that is needed to be analyzed comprehensively. On the basis of medicine, different tests and various scan techniques are utilized to efficiently make a decision. On the basis of machine learning, two phenomena come into prominence: 1-Qalitative data, 2-Framework design to detect the necessary information among the data.In this paper, an efficient framework is proposed to reveal the heart failure on the specific data. Three optimized classifiers were compared to assign the classification unit of framework. Manuel selection and filter based-feature ranking methods were considered to determine the necessary information and to reveal the heart failure. In experiments, two-fold cross validation was utilized as the test method to force the classifiers, and seven metrics based-comparisons were realized to objectively choose the features and classifiers. Consequently, the best framework achieved remarkable scores of 86.62% (accuracy), 83.01% (AUC), 72.92% (sensitivity), 93.10% (specificity), 82.39% (g-mean), 83.33% (precision) and 77.78% (f-measure) for survival prediction on heart failure clinical records. © 2021 IEEE.
Description: 5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021 -- 21 October 2021 through 23 October 2021 -- -- 174473
ISBN: 9781665449304
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

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