Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1645
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Koyuncu, Hasan | - |
dc.date.accessioned | 2022-01-30T17:32:50Z | - |
dc.date.available | 2022-01-30T17:32:50Z | - |
dc.date.issued | 2021 | - |
dc.identifier.isbn | 9781665449304 | - |
dc.identifier.uri | https://doi.org/10.1109/ISMSIT52890.2021.9604687 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1645 | - |
dc.description | 5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021 -- 21 October 2021 through 23 October 2021 -- -- 174473 | en_US |
dc.description.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. | en_US |
dc.description.sponsorship | This work is supported by the Coordinatorship of Konya Technical University's Scientific Research Projects. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | ISMSIT 2021 - 5th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | chaotic | en_US |
dc.subject | framework design | en_US |
dc.subject | gauss map | en_US |
dc.subject | heart failure | en_US |
dc.subject | optimized classifier | en_US |
dc.subject | pattern classification | en_US |
dc.subject | Classification (of information) | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Heart | en_US |
dc.subject | Testing | en_US |
dc.subject | Chaotics | en_US |
dc.subject | Failure detection | en_US |
dc.subject | Feature classifiers | en_US |
dc.subject | Framework designs | en_US |
dc.subject | Gauss maps | en_US |
dc.subject | Heart failure | en_US |
dc.subject | Hybrid classifier | en_US |
dc.subject | Optimized classifier | en_US |
dc.subject | Patterns classification | en_US |
dc.subject | Scan techniques | en_US |
dc.subject | Cardiology | en_US |
dc.title | A Framework Design for Heart Failure Detection: Analyzes on Features and Hybrid Classifiers | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/ISMSIT52890.2021.9604687 | - |
dc.identifier.scopus | 2-s2.0-85123300864 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.startpage | 62 | en_US |
dc.identifier.endpage | 67 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 55884277600 | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
item.fulltext | With Fulltext | - |
item.openairetype | Conference Object | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
Files in This Item:
File | Size | Format | |
---|---|---|---|
A_Framework_Design_for_Heart_Failure_Detection_Analyzes_on_Features_and_Hybrid_Classifiers.pdf Until 2030-01-01 | 2.95 MB | Adobe PDF | View/Open Request a copy |
CORE Recommender
Page view(s)
134
checked on Sep 9, 2024
Download(s)
8
checked on Sep 9, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.