An accurate fuzzy rule-based classification systems for heart disease diagnosis

Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clin...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Khalid Bahani [verfasserIn] Mohammed Moujabbir [verfasserIn] Mohammed Ramdani [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Heart disease prediction Fuzzy rule-based classification systems Classification rules learning Machine learning Explainable artificial intelligence

Übergeordnetes Werk:	In: Scientific African - Elsevier, 2018, 14(2021), Seite e01019-
Übergeordnetes Werk:	volume:14 ; year:2021 ; pages:e01019-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.sciaf.2021.e01019

Katalog-ID:	DOAJ018783961

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ018783961
003	DE-627
005	20230310102837.0
007	cr uuu---uuuuu
008	230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.sciaf.2021.e01019 \|2 doi
035			\|a (DE-627)DOAJ018783961
035			\|a (DE-599)DOAJa912020d484d423da65a7d69a0920d3a
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Khalid Bahani \|e verfasserin \|4 aut
245	1	3	\|a An accurate fuzzy rule-based classification systems for heart disease diagnosis
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision.
650		4	\|a Heart disease prediction
650		4	\|a Fuzzy rule-based classification systems
650		4	\|a Classification rules learning
650		4	\|a Machine learning
650		4	\|a Explainable artificial intelligence
653		0	\|a Science
653		0	\|a Q
700	0		\|a Mohammed Moujabbir \|e verfasserin \|4 aut
700	0		\|a Mohammed Ramdani \|e verfasserin \|4 aut
773	0	8	\|i In \|t Scientific African \|d Elsevier, 2018 \|g 14(2021), Seite e01019- \|w (DE-627)1047200163 \|x 24682276 \|7 nnns
773	1	8	\|g volume:14 \|g year:2021 \|g pages:e01019-
856	4	0	\|u https://doi.org/10.1016/j.sciaf.2021.e01019 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/a912020d484d423da65a7d69a0920d3a \|z kostenfrei
856	4	0	\|u http://www.sciencedirect.com/science/article/pii/S2468227621003203 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2468-2276 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 14 \|j 2021 \|h e01019-

Indexfelder

author_variant	k b kb m m mm m r mr
matchkey_str	article:24682276:2021----::ncuaeuzrlbsdlsiiainytmfre
hierarchy_sort_str	2021
publishDate	2021
allfields	10.1016/j.sciaf.2021.e01019 doi (DE-627)DOAJ018783961 (DE-599)DOAJa912020d484d423da65a7d69a0920d3a DE-627 ger DE-627 rakwb eng Khalid Bahani verfasserin aut An accurate fuzzy rule-based classification systems for heart disease diagnosis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision. Heart disease prediction Fuzzy rule-based classification systems Classification rules learning Machine learning Explainable artificial intelligence Science Q Mohammed Moujabbir verfasserin aut Mohammed Ramdani verfasserin aut In Scientific African Elsevier, 2018 14(2021), Seite e01019- (DE-627)1047200163 24682276 nnns volume:14 year:2021 pages:e01019- https://doi.org/10.1016/j.sciaf.2021.e01019 kostenfrei https://doaj.org/article/a912020d484d423da65a7d69a0920d3a kostenfrei http://www.sciencedirect.com/science/article/pii/S2468227621003203 kostenfrei https://doaj.org/toc/2468-2276 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 e01019-
spelling	10.1016/j.sciaf.2021.e01019 doi (DE-627)DOAJ018783961 (DE-599)DOAJa912020d484d423da65a7d69a0920d3a DE-627 ger DE-627 rakwb eng Khalid Bahani verfasserin aut An accurate fuzzy rule-based classification systems for heart disease diagnosis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision. Heart disease prediction Fuzzy rule-based classification systems Classification rules learning Machine learning Explainable artificial intelligence Science Q Mohammed Moujabbir verfasserin aut Mohammed Ramdani verfasserin aut In Scientific African Elsevier, 2018 14(2021), Seite e01019- (DE-627)1047200163 24682276 nnns volume:14 year:2021 pages:e01019- https://doi.org/10.1016/j.sciaf.2021.e01019 kostenfrei https://doaj.org/article/a912020d484d423da65a7d69a0920d3a kostenfrei http://www.sciencedirect.com/science/article/pii/S2468227621003203 kostenfrei https://doaj.org/toc/2468-2276 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 e01019-
allfields_unstemmed	10.1016/j.sciaf.2021.e01019 doi (DE-627)DOAJ018783961 (DE-599)DOAJa912020d484d423da65a7d69a0920d3a DE-627 ger DE-627 rakwb eng Khalid Bahani verfasserin aut An accurate fuzzy rule-based classification systems for heart disease diagnosis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision. Heart disease prediction Fuzzy rule-based classification systems Classification rules learning Machine learning Explainable artificial intelligence Science Q Mohammed Moujabbir verfasserin aut Mohammed Ramdani verfasserin aut In Scientific African Elsevier, 2018 14(2021), Seite e01019- (DE-627)1047200163 24682276 nnns volume:14 year:2021 pages:e01019- https://doi.org/10.1016/j.sciaf.2021.e01019 kostenfrei https://doaj.org/article/a912020d484d423da65a7d69a0920d3a kostenfrei http://www.sciencedirect.com/science/article/pii/S2468227621003203 kostenfrei https://doaj.org/toc/2468-2276 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 e01019-
allfieldsGer	10.1016/j.sciaf.2021.e01019 doi (DE-627)DOAJ018783961 (DE-599)DOAJa912020d484d423da65a7d69a0920d3a DE-627 ger DE-627 rakwb eng Khalid Bahani verfasserin aut An accurate fuzzy rule-based classification systems for heart disease diagnosis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision. Heart disease prediction Fuzzy rule-based classification systems Classification rules learning Machine learning Explainable artificial intelligence Science Q Mohammed Moujabbir verfasserin aut Mohammed Ramdani verfasserin aut In Scientific African Elsevier, 2018 14(2021), Seite e01019- (DE-627)1047200163 24682276 nnns volume:14 year:2021 pages:e01019- https://doi.org/10.1016/j.sciaf.2021.e01019 kostenfrei https://doaj.org/article/a912020d484d423da65a7d69a0920d3a kostenfrei http://www.sciencedirect.com/science/article/pii/S2468227621003203 kostenfrei https://doaj.org/toc/2468-2276 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 e01019-
allfieldsSound	10.1016/j.sciaf.2021.e01019 doi (DE-627)DOAJ018783961 (DE-599)DOAJa912020d484d423da65a7d69a0920d3a DE-627 ger DE-627 rakwb eng Khalid Bahani verfasserin aut An accurate fuzzy rule-based classification systems for heart disease diagnosis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision. Heart disease prediction Fuzzy rule-based classification systems Classification rules learning Machine learning Explainable artificial intelligence Science Q Mohammed Moujabbir verfasserin aut Mohammed Ramdani verfasserin aut In Scientific African Elsevier, 2018 14(2021), Seite e01019- (DE-627)1047200163 24682276 nnns volume:14 year:2021 pages:e01019- https://doi.org/10.1016/j.sciaf.2021.e01019 kostenfrei https://doaj.org/article/a912020d484d423da65a7d69a0920d3a kostenfrei http://www.sciencedirect.com/science/article/pii/S2468227621003203 kostenfrei https://doaj.org/toc/2468-2276 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 e01019-
language	English
source	In Scientific African 14(2021), Seite e01019- volume:14 year:2021 pages:e01019-
sourceStr	In Scientific African 14(2021), Seite e01019- volume:14 year:2021 pages:e01019-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Heart disease prediction Fuzzy rule-based classification systems Classification rules learning Machine learning Explainable artificial intelligence Science Q
isfreeaccess_bool	true
container_title	Scientific African
authorswithroles_txt_mv	Khalid Bahani @@aut@@ Mohammed Moujabbir @@aut@@ Mohammed Ramdani @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	1047200163
id	DOAJ018783961
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ018783961</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310102837.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.sciaf.2021.e01019</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ018783961</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa912020d484d423da65a7d69a0920d3a</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Khalid Bahani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An accurate fuzzy rule-based classification systems for heart disease diagnosis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heart disease prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy rule-based classification systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification rules learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Explainable artificial intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammed Moujabbir</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammed Ramdani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Scientific African</subfield><subfield code="d">Elsevier, 2018</subfield><subfield code="g">14(2021), Seite e01019-</subfield><subfield code="w">(DE-627)1047200163</subfield><subfield code="x">24682276</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:e01019-</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.sciaf.2021.e01019</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a912020d484d423da65a7d69a0920d3a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S2468227621003203</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2468-2276</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2086</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2021</subfield><subfield code="h">e01019-</subfield></datafield></record></collection>
author	Khalid Bahani
spellingShingle	Khalid Bahani misc Heart disease prediction misc Fuzzy rule-based classification systems misc Classification rules learning misc Machine learning misc Explainable artificial intelligence misc Science misc Q An accurate fuzzy rule-based classification systems for heart disease diagnosis
authorStr	Khalid Bahani
ppnlink_with_tag_str_mv	@@773@@(DE-627)1047200163
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	24682276
topic_title	An accurate fuzzy rule-based classification systems for heart disease diagnosis Heart disease prediction Fuzzy rule-based classification systems Classification rules learning Machine learning Explainable artificial intelligence
topic	misc Heart disease prediction misc Fuzzy rule-based classification systems misc Classification rules learning misc Machine learning misc Explainable artificial intelligence misc Science misc Q
topic_unstemmed	misc Heart disease prediction misc Fuzzy rule-based classification systems misc Classification rules learning misc Machine learning misc Explainable artificial intelligence misc Science misc Q
topic_browse	misc Heart disease prediction misc Fuzzy rule-based classification systems misc Classification rules learning misc Machine learning misc Explainable artificial intelligence misc Science misc Q
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Scientific African
hierarchy_parent_id	1047200163
hierarchy_top_title	Scientific African
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)1047200163
title	An accurate fuzzy rule-based classification systems for heart disease diagnosis
ctrlnum	(DE-627)DOAJ018783961 (DE-599)DOAJa912020d484d423da65a7d69a0920d3a
title_full	An accurate fuzzy rule-based classification systems for heart disease diagnosis
author_sort	Khalid Bahani
journal	Scientific African
journalStr	Scientific African
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
author_browse	Khalid Bahani Mohammed Moujabbir Mohammed Ramdani
container_volume	14
format_se	Elektronische Aufsätze
author-letter	Khalid Bahani
doi_str_mv	10.1016/j.sciaf.2021.e01019
author2-role	verfasserin
title_sort	accurate fuzzy rule-based classification systems for heart disease diagnosis
title_auth	An accurate fuzzy rule-based classification systems for heart disease diagnosis
abstract	Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision.
abstractGer	Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision.
abstract_unstemmed	Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	An accurate fuzzy rule-based classification systems for heart disease diagnosis
url	https://doi.org/10.1016/j.sciaf.2021.e01019 https://doaj.org/article/a912020d484d423da65a7d69a0920d3a http://www.sciencedirect.com/science/article/pii/S2468227621003203 https://doaj.org/toc/2468-2276
remote_bool	true
author2	Mohammed Moujabbir Mohammed Ramdani
author2Str	Mohammed Moujabbir Mohammed Ramdani
ppnlink	1047200163
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1016/j.sciaf.2021.e01019
up_date	2024-07-03T19:57:17.783Z
_version_	1803589137510432768
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ018783961</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310102837.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.sciaf.2021.e01019</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ018783961</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa912020d484d423da65a7d69a0920d3a</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Khalid Bahani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An accurate fuzzy rule-based classification systems for heart disease diagnosis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heart disease prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy rule-based classification systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification rules learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Explainable artificial intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammed Moujabbir</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammed Ramdani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Scientific African</subfield><subfield code="d">Elsevier, 2018</subfield><subfield code="g">14(2021), Seite e01019-</subfield><subfield code="w">(DE-627)1047200163</subfield><subfield code="x">24682276</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:e01019-</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.sciaf.2021.e01019</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a912020d484d423da65a7d69a0920d3a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S2468227621003203</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2468-2276</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2086</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2021</subfield><subfield code="h">e01019-</subfield></datafield></record></collection>
score	7.401787

Nicht das Richtige dabei?

Schreiben Sie uns!

An accurate fuzzy rule-based classification systems for heart disease diagnosis

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?