Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach

Miray Akgül; Özlen Erkal Sönmez; Tuncay Özcan

doi:10.1007/978-3-030-23756-1_147

Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach

Miray Akgül, Özlen Erkal Sönmez^*, Tuncay Özcan

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

12 Atıf (Scopus)

Özet

Heart disease is the most important public health problem for many countries. Early diagnosis of heart disease is extremely crucial for the survival of the patient. At this point, classification algorithms are widely used for medical diagnosis. In this study, firstly, artificial neural network (ANN) with default parameters is used to diagnose heart disease. Then, a hybrid approach, combining artificial neural network (ANN) and genetic algorithm (GA), is proposed to improve classification accuracy. Finally, the effectiveness of the proposed approach is illustrated with ‘Cleveland’ dataset taken from UCI machine learning repository. Experimental results show that the proposed hybrid ANN - GA approach outperforms Naive Bayes, K- Nearest Neighbor and C4.5 algorithms in terms of accuracy rate, precision, recall and F-measure.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editörler	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Yayınlayan	Springer Verlag
Sayfalar	1250-1257
Sayfa sayısı	8
ISBN (Basılı)	9783030237554
DOI'lar	https://doi.org/10.1007/978-3-030-23756-1_147
Yayın durumu	Yayınlandı - 2020
Harici olarak yayınlandı	Evet
Etkinlik	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Süre: 23 Tem 2019 → 25 Tem 2019

Yayın serisi

Adı	Advances in Intelligent Systems and Computing
Hacim	1029
ISSN (Basılı)	2194-5357
ISSN (Elektronik)	2194-5365

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	23/07/19 → 25/07/19

Bibliyografik not

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

BM SKH

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Belgeye Erişim

10.1007/978-3-030-23756-1_147

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Akgül, M., Sönmez, Ö. E., & Özcan, T. (2020). Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 1250-1257). (Advances in Intelligent Systems and Computing; Hac. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_147

Akgül, Miray ; Sönmez, Özlen Erkal ; Özcan, Tuncay. / Diagnosis of heart disease using an intelligent method : A hybrid ANN – GA approach. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editör / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 1250-1257 (Advances in Intelligent Systems and Computing).

@inproceedings{a6380c4cf31c477ea0e99e462e344cd7,

title = "Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach",

abstract = "Heart disease is the most important public health problem for many countries. Early diagnosis of heart disease is extremely crucial for the survival of the patient. At this point, classification algorithms are widely used for medical diagnosis. In this study, firstly, artificial neural network (ANN) with default parameters is used to diagnose heart disease. Then, a hybrid approach, combining artificial neural network (ANN) and genetic algorithm (GA), is proposed to improve classification accuracy. Finally, the effectiveness of the proposed approach is illustrated with {\textquoteleft}Cleveland{\textquoteright} dataset taken from UCI machine learning repository. Experimental results show that the proposed hybrid ANN - GA approach outperforms Naive Bayes, K- Nearest Neighbor and C4.5 algorithms in terms of accuracy rate, precision, recall and F-measure.",

keywords = "Artificial neural network, Classification, Genetic algorithm, Heart disease",

author = "Miray Akg{\"u}l and S{\"o}nmez, {{\"O}zlen Erkal} and Tuncay {\"O}zcan",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

year = "2020",

doi = "10.1007/978-3-030-23756-1_147",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "1250--1257",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and A.Cagri Tolga",

booktitle = "Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference",

address = "Germany",

}

Akgül, M, Sönmez, ÖE & Özcan, T 2020, Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, hac. 1029, Springer Verlag, pp. 1250-1257, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_147

Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach. / Akgül, Miray; Sönmez, Özlen Erkal; Özcan, Tuncay.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 1250-1257 (Advances in Intelligent Systems and Computing; Hac. 1029).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Diagnosis of heart disease using an intelligent method

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2019

AU - Akgül, Miray

AU - Sönmez, Özlen Erkal

AU - Özcan, Tuncay

PY - 2020

Y1 - 2020

N2 - Heart disease is the most important public health problem for many countries. Early diagnosis of heart disease is extremely crucial for the survival of the patient. At this point, classification algorithms are widely used for medical diagnosis. In this study, firstly, artificial neural network (ANN) with default parameters is used to diagnose heart disease. Then, a hybrid approach, combining artificial neural network (ANN) and genetic algorithm (GA), is proposed to improve classification accuracy. Finally, the effectiveness of the proposed approach is illustrated with ‘Cleveland’ dataset taken from UCI machine learning repository. Experimental results show that the proposed hybrid ANN - GA approach outperforms Naive Bayes, K- Nearest Neighbor and C4.5 algorithms in terms of accuracy rate, precision, recall and F-measure.

AB - Heart disease is the most important public health problem for many countries. Early diagnosis of heart disease is extremely crucial for the survival of the patient. At this point, classification algorithms are widely used for medical diagnosis. In this study, firstly, artificial neural network (ANN) with default parameters is used to diagnose heart disease. Then, a hybrid approach, combining artificial neural network (ANN) and genetic algorithm (GA), is proposed to improve classification accuracy. Finally, the effectiveness of the proposed approach is illustrated with ‘Cleveland’ dataset taken from UCI machine learning repository. Experimental results show that the proposed hybrid ANN - GA approach outperforms Naive Bayes, K- Nearest Neighbor and C4.5 algorithms in terms of accuracy rate, precision, recall and F-measure.

KW - Artificial neural network

KW - Classification

KW - Genetic algorithm

KW - Heart disease

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U2 - 10.1007/978-3-030-23756-1_147

DO - 10.1007/978-3-030-23756-1_147

M3 - Conference contribution

AN - SCOPUS:85069479385

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 1250

EP - 1257

BT - Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

Y2 - 23 July 2019 through 25 July 2019

ER -

Akgül M, Sönmez ÖE, Özcan T. Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editörler, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 1250-1257. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_147

Diagnosis of heart disease using an intelligent method: A hybrid ANN – GA approach

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