Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease
| dc.contributor.author | Kolukisa, Burak | |
| dc.contributor.author | Hacilar, Hilal | |
| dc.contributor.author | Göy, Gökhan | |
| dc.contributor.author | Kus, Mustafa | |
| dc.contributor.author | Bakir-Güngör, Burcu | |
| dc.contributor.author | Aral, Atilla | |
| dc.contributor.author | Güngör, Vehbi Çağrı | |
| dc.date.accessioned | 2025-09-25T10:46:35Z | |
| dc.date.available | 2025-09-25T10:46:35Z | |
| dc.date.issued | 2018 | |
| dc.description | Baidu; et al.; Expedia Group; IEEE; IEEE Computer Society; Squirrel AI Learning | en_US |
| dc.description | Hacilar, Hilal/0000-0002-5811-6722; | en_US |
| dc.description.abstract | According to the World Health Organization (WHO), 31% of the world's total deaths in 2016 (17.9 million) was due to cardiovascular diseases (CVD). With the development of information technologies, it has become possible to predict whether people have heart diseases or not by checking certain physical and biochemical values at a lower cost. In this study, we have evalated a set of different classification algorithms, linear discriminant analysis and proposed a new hybrid feature selection methodology for the diagnosis of coronary heart diseases (CHD). Throughout this research effort, using three publicly available Heart Disease diagnosis datasets (UCI Machine Learning Repository), we have conducted comparative performance evaluations in terms of accuracy, sensitivity, specificity, F-measure, AUC and running time. © 2023 Elsevier B.V., All rights reserved. | en_US |
| dc.description.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK) [3180177] | en_US |
| dc.description.sponsorship | This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project no 3180177. | en_US |
| dc.description.sponsorship | Baidu; et al.; Expedia Group; IEEE; IEEE Computer Society; Squirrel AI Learning | |
| dc.identifier.doi | 10.1109/BigData.2018.8622609 | |
| dc.identifier.isbn | 9781538650356 | |
| dc.identifier.issn | 2639-1589 | |
| dc.identifier.scopus | 2-s2.0-85062604971 | |
| dc.identifier.uri | https://doi.org/10.1109/BigData.2018.8622609 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12573/3796 | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | -- 2018 IEEE International Conference on Big Data, Big Data 2018 -- Seattle; WA -- 144531 | en_US |
| dc.relation.ispartofseries | IEEE International Conference on Big Data | |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Cardiovascular Disease | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Feature Selection | en_US |
| dc.subject | Linear Discriminant Analysis | en_US |
| dc.subject | Cardiology | en_US |
| dc.subject | Computer Aided Diagnosis | en_US |
| dc.subject | Discriminant Analysis | en_US |
| dc.subject | Diseases | en_US |
| dc.subject | Feature Selection | en_US |
| dc.subject | Heart | en_US |
| dc.subject | Biochemical Values | en_US |
| dc.subject | Cardiovascular Disease | en_US |
| dc.subject | Classification Algorithm | en_US |
| dc.subject | Coronary Artery Disease | en_US |
| dc.subject | Features Selection | en_US |
| dc.subject | Heart Disease | en_US |
| dc.subject | Hybrid Feature Selections | en_US |
| dc.subject | Linear Discriminant Analyze | en_US |
| dc.subject | Low-Costs | en_US |
| dc.subject | World Health Organization | en_US |
| dc.subject | Data Mining | en_US |
| dc.title | Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease | en_US |
| dc.type | Conference Object | en_US |
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| gdc.author.id | Hacilar, Hilal/0000-0002-5811-6722 | |
| gdc.author.id | Bakir-Gungor, Burcu/0000-0002-2272-6270 | |
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| gdc.author.wosid | Aral, Atilla/Kma-3093-2024 | |
| gdc.author.wosid | Hacılar, Hilal/Hgu-9217-2022 | |
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| gdc.description.department | Abdullah Gül University | en_US |
| gdc.description.departmenttemp | [Kolukisa] Burak, Department of Computer Engineering, Abdullah Gül Üniversitesi, Kayseri, Turkey; [Hacilar] Hilal, Department of Computer Engineering, Abdullah Gül Üniversitesi, Kayseri, Turkey; [Göy] Gökhan, Department of Computer Engineering, Abdullah Gül Üniversitesi, Kayseri, Turkey; [Kus] Mustafa, Keydata Bilgi Işlem Teknoloji Sistemleri A.Ş., Ankara, Turkey; [Bakir-Güngör] Burcu, Department of Computer Engineering, Abdullah Gül Üniversitesi, Kayseri, Turkey; [Aral] Atilla, Department of Cardiovascular Surgery, Ankara Üniversitesi, Ankara, Turkey; [Güngör] Vehbi Çağrı, Department of Computer Engineering, Abdullah Gül Üniversitesi, Kayseri, Turkey | en_US |
| gdc.description.endpage | 2238 | en_US |
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