Araştırma Makalesi
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Yıl 2021, Cilt 7, Sayı 1, 1 - 8, 31.03.2021
https://doi.org/10.22399/ijcesen.837731

Öz

Kaynakça

  • Referans1 Abbod, M.F.,vonKeyserlingk, D.G., Linkens, D.A., Mahfouf, M., (2001), Survey of utilisation of fuzzy technology in medicine and healthcare, Fuzzy Sets and Systems, Volume 120, Issue 2, 331-349
  • Referans2 Adeli, A., &Neshat, M. (2010), A fuzzy expert system for heart disease diagnosis, Proceedings of the International MultiConference of Engineers and Computer Scientists Hong Kong. March.
  • Referans3 Ali, F., El-Sappagh, S., (2020) Riazul Islam, S.M., Kwak, D., Ali, A., Imran, M., Kwak, K.S., A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion, Information Fusion 63 , 208-222
  • Referans4 Anooj, P.K., (2012), Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules, Journal of King Saud University – Computer and Information Sciences, Volume 24, Issue 1, 27-40
  • Referans5 Baykal, N., Beyan, T., (2004), Bulanık Mantık İlke ve Temelleri, Ankara: Bıçaklar Kitabevi.
  • Referans6 Bhatla, N., &Kiran, J., (2012), A novel approach for heart disease diagnosis using data mining and fuzzy logic, International Journal of Computer Applications, 54(17), 16–21.
  • Referans7 Biyouki, S.A., Turksen, I.B., &Fazel Zarandi, M.H. (2015), Fuzzy rule-based expert system for diagnosis of thyroid disease, In Proceedings of 2015 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB) pp. 1–7 IEEE.
  • Referans8 Danish, E.,Onder, M., (2020), Application of Fuzzy Logic for Predicting of Mine Fire in Underground Coal Mine, Safety and Health at Work, Volume 11, Issue 3, 322-334
  • Referans9 Demirhan, A., Kılıç, Y.A., Güler, İ. , (2010), Tıpta Yapay Zeka Uygulamaları, Artificial Intelligence Applications in Medicine, 9(1), 31-41
  • Referans10 Devlin, R. J.,& Henry, J. A., (2008), Clinical review: Major consequences of illicit drug consumption, Critical Care, 12(1), 202.
  • Referans11 Devi, Y.N., &Anto, S., (2014), An evolutionary-fuzzy expert system for the diagnosis of coronary artery disease, International Journal of Bio-Science and Bio-Technology, 3(4), 1478-1484.
  • Referans12 Ertunç, H.M., (2012), Introductıon To Fuzzy Logıc,. Kocaeli Üniversitesi Mekatronik Mühendisliği.
  • Referans13 Güleç, S., (2009), Kalp Damar Hastalıklarında Global Risk Ve Hedefler, Arch Turk Soc Cardiol, 37(2),1-10.
  • Referans14 Jensen, G.,Nyboe, J., Appleyard, M., Schnohr, P., (1991), Risk factors for acute myocardial infarction in Copenhagen, II: Smoking, alcohol intake, physical activity, obesity, oral contraception, diabetes, lipids, and blood pressure, Europen Heart Journal, Volume 12, Issue 3, 298¬-308.
  • Referans15 Kannel, W.B., D’Agostino, R.B., Sullivan, L., Wilson, P.W.F., (2004), Concept and usefulness of cardiovascular risk profiles, American Heart Journal, 148, 16-26.
  • Referans16 Kasapoğlu, E.S., Enç, N., (2017) A Guide for Coronary Artery Patients, Journal of Cardiovascular Nursing, 8(15), 1-7
  • Referans17 Keskenler, M.F., Keskenler, E.F., (2017), Bulanık Mantığın Tarihi Gelişimi, Takvim-i Vekayi, 5(1), 1-10
  • Referans18 Kosuge, M.,Kimura, K., Ishikawa, T., Ebina, T., Hibi, K., Tsukahara, K., Kanna, M., Iwahashi, N., Okuda, J., Nozawa, N., Ozaki, H., Yano, H., Nakati, T., Kusama, I., Umemura, S., (2006) Differences Between Men and Women in Terms of Clinical Features of ST-Segment Elevation Acute Myocardial Infarction, Circulation Journal, Volume 70, Issue 3, 222-226
  • Referans19 Kumar, S., Kaur, G., (2013), Detection of Heart Diseases using Fuzzy Logic, International Journal of Engineering Trends and Technology (IJETT) , Volume 4, Issue 6, 2694-2699
  • Referans20 Lee, C,S., & Wang, M.H., (2011), A fuzzy expert system for diabetes decision support application, IEEE Transactions on Systems, Man, and Cybernetics, 41(1), 139-153
  • Referans21 Lu, L., Liu, M., Sun, R., Zheng, Y., Zhang, P., (2015), Myocardial Infarction: Symptoms and Treatments, Cell Biochem Biophys 72, 865–867
  • Referans22 Malmir, B., Amini, M., Chang, S.I., (2017), A medical decision support system for disease diagnosis under uncertainty, Expert Systems With Applications 88, 95-108
  • Referans23 Mamdani, E.H., Assilian, S., (1975), An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, Volume 7, Issue 1, 1-13
  • Referans24 Nilashi, M., Ibrahım, O., Ahmadi, H., Shahmoradi, L., (2017), A knowledge-based system for breast cancer classification using fuzzy logic method, Telematics and Informatics, Volume 34, Issue 4, 133-144
  • Referans25 Onat, A., (2001), Risk factors and cardiovascular disease in Turkey, Atherosclerosis, 156, 1-10.
  • Referans26 Palaniappan, S., Awang, R., (2008), Intelligent heart disease prediction system using data mining techniques, International Journal of Computer Science and Network Security, 8 (8), 108–115.
  • Referans27 Patil, S.B., Kumaraswamy, Y.S., (2009), Intelligent and effective heart attack prediction system using data mining and artificial neural network, European Journal of Scientific Research, 31 (4), 642–656.
  • Referans28 Phuong, N.H., Kreinovich, V., (2001), Fuzzy logic and its applications in medicine, International Journal of Medical Informatics, Volume 62, Issues 2-3, 165-173
  • Referans29 Ross, T.J, (2016), Fuzzy Logic with Engineering Applications, New York: Wiley-Blackwell.
  • Referans 30 Rustempasic, I., & Can, M. (2013), Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition, Southeast Europe Journal of Soft Computing, 2(1), 42–49.
  • Referans31 Sağlık Bakanlığı (2010), Türkiye kalp ve damar hastalıklarını önleme ve kontrol programı. Birincil, ikincil, üçüncül korumaya yönelik stratejik plan ve eylem planı (2010-2014), T.C. Sağlık Bakanlığı, Temel Sağlık Hizmetleri Genel Müdürlüğü. Yayın No: 812. Ankara. Anıl Matbaası. (4-30).
  • Referans32 Saikia, D., & Dutta, J.C., (2016), Early diagnosis of dengue disease using fuzzy inference system, in Proceedings of 2016 international conference on microelectronics, computing and communications (MicroCom) pp. 1–6. IEEE.
  • Referans33 Samuel, O. W., Omisore, M. O., &Ojokoh, B.A.(2013), A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever, Expert Systems with Applications, 40(10), 4164–4171.
  • Referans34 Sarı, M., Murat, Y., Kırabalı, M., (2005), Bulanık Modelleme Yaklaşımı Ve Uygulamaları, Journal of Science and Technology of Dumlupınar University , (009) , 77-92
  • Referans35 Syropoulos, A., Grammenos, T., (2020), A Modern Introduction to Fuzzy Mathematics, New York: Wiley
  • Referans36 Şahinler, S., Görgülü, Ö.,Bek, Yüksel., (2006), Sağlık Alanında Bulanık Mantık Yöntemlerinin Uygulanabilirliği ,IX. Ulusal Biyoistatistik Kongresi, Zonguldak 2006
  • Referans37 T.C. Sağlık Bakanlığı, Türkiye Kalp ve Damar Hastalıkları Önleme ve Kontrol Programı Eylem Planı (2015-2020), erişim tarihi: 9 Ekim 2020 https://tkd.org.tr/TKDData/Uploads/files/Turkiye-kalp-ve-damar-hastaliklari-onleme-ve-kontrol-programi.pdf
  • Referans38 Thakur, S., Raw, S.N., & Sharma, R., (2016), Design of a fuzzy model for thalassemia disease diagnosis: Using mamdani type fuzzy inference system, International Journal of Pharmacy and Pharmaceutical Sciences, 8(4), 356-361.
  • Referans39 Torun, S., (2007), Koroner Kalp Hastalığı Riski Tanısı Ve Tedavisi İçin Hiyerarşik Bir Bulanık Uzman Sistem Tasarımı, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi
  • Referans40 World Health Organization, (2017), Cardiovascular diseases (CVDs): key facts. Erişim tarihi: 9 Ekim 2020 https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
  • Referans41 World Health Organization, (2020), Tobacco responsible for 20% of deaths from coronary heart disease, erişim tarihi: 10 Ekim 2020 https://www.who.int/news/detail/22-09-2020-tobacco-responsible-for-20-of-deaths-from-coronary-heart-disease
  • Referans42 Zadeh, L.A., (1965), Fuzzy Sets, Information and Control, Volume 8, Issue 3, 338-353

Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks

Yıl 2021, Cilt 7, Sayı 1, 1 - 8, 31.03.2021
https://doi.org/10.22399/ijcesen.837731

Öz

As a result of the researches, it has been revealed that heart attack is the number one cause of death in the world. This problem will continue to increase, especially today and in the future. In this study, a heart attack was predicted by considering the factors affecting heart attack. Due to the uncertain conditions in heart attack, the fuzzy logic method, which is frequently used in healthcare, was used and expert opinions were taken into account in the model created. 576 rules were defined using the Mamdani fuzzy inference method. The study was tested with 10 patient data and the results were compared with the actual values. In addition, multiple regression analysis was performed, variables that had a significant effect on heart attack were determined, and the relationship between dependent and independent variables was examined. It was shown in the study that dependent variables explained the independent variable by 41.9% thanks to the multiple regression analysis. The regression equation obtained in line with these results significantly predicted the heart attack and the effect levels of the independent variables were determined.

Kaynakça

  • Referans1 Abbod, M.F.,vonKeyserlingk, D.G., Linkens, D.A., Mahfouf, M., (2001), Survey of utilisation of fuzzy technology in medicine and healthcare, Fuzzy Sets and Systems, Volume 120, Issue 2, 331-349
  • Referans2 Adeli, A., &Neshat, M. (2010), A fuzzy expert system for heart disease diagnosis, Proceedings of the International MultiConference of Engineers and Computer Scientists Hong Kong. March.
  • Referans3 Ali, F., El-Sappagh, S., (2020) Riazul Islam, S.M., Kwak, D., Ali, A., Imran, M., Kwak, K.S., A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion, Information Fusion 63 , 208-222
  • Referans4 Anooj, P.K., (2012), Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules, Journal of King Saud University – Computer and Information Sciences, Volume 24, Issue 1, 27-40
  • Referans5 Baykal, N., Beyan, T., (2004), Bulanık Mantık İlke ve Temelleri, Ankara: Bıçaklar Kitabevi.
  • Referans6 Bhatla, N., &Kiran, J., (2012), A novel approach for heart disease diagnosis using data mining and fuzzy logic, International Journal of Computer Applications, 54(17), 16–21.
  • Referans7 Biyouki, S.A., Turksen, I.B., &Fazel Zarandi, M.H. (2015), Fuzzy rule-based expert system for diagnosis of thyroid disease, In Proceedings of 2015 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB) pp. 1–7 IEEE.
  • Referans8 Danish, E.,Onder, M., (2020), Application of Fuzzy Logic for Predicting of Mine Fire in Underground Coal Mine, Safety and Health at Work, Volume 11, Issue 3, 322-334
  • Referans9 Demirhan, A., Kılıç, Y.A., Güler, İ. , (2010), Tıpta Yapay Zeka Uygulamaları, Artificial Intelligence Applications in Medicine, 9(1), 31-41
  • Referans10 Devlin, R. J.,& Henry, J. A., (2008), Clinical review: Major consequences of illicit drug consumption, Critical Care, 12(1), 202.
  • Referans11 Devi, Y.N., &Anto, S., (2014), An evolutionary-fuzzy expert system for the diagnosis of coronary artery disease, International Journal of Bio-Science and Bio-Technology, 3(4), 1478-1484.
  • Referans12 Ertunç, H.M., (2012), Introductıon To Fuzzy Logıc,. Kocaeli Üniversitesi Mekatronik Mühendisliği.
  • Referans13 Güleç, S., (2009), Kalp Damar Hastalıklarında Global Risk Ve Hedefler, Arch Turk Soc Cardiol, 37(2),1-10.
  • Referans14 Jensen, G.,Nyboe, J., Appleyard, M., Schnohr, P., (1991), Risk factors for acute myocardial infarction in Copenhagen, II: Smoking, alcohol intake, physical activity, obesity, oral contraception, diabetes, lipids, and blood pressure, Europen Heart Journal, Volume 12, Issue 3, 298¬-308.
  • Referans15 Kannel, W.B., D’Agostino, R.B., Sullivan, L., Wilson, P.W.F., (2004), Concept and usefulness of cardiovascular risk profiles, American Heart Journal, 148, 16-26.
  • Referans16 Kasapoğlu, E.S., Enç, N., (2017) A Guide for Coronary Artery Patients, Journal of Cardiovascular Nursing, 8(15), 1-7
  • Referans17 Keskenler, M.F., Keskenler, E.F., (2017), Bulanık Mantığın Tarihi Gelişimi, Takvim-i Vekayi, 5(1), 1-10
  • Referans18 Kosuge, M.,Kimura, K., Ishikawa, T., Ebina, T., Hibi, K., Tsukahara, K., Kanna, M., Iwahashi, N., Okuda, J., Nozawa, N., Ozaki, H., Yano, H., Nakati, T., Kusama, I., Umemura, S., (2006) Differences Between Men and Women in Terms of Clinical Features of ST-Segment Elevation Acute Myocardial Infarction, Circulation Journal, Volume 70, Issue 3, 222-226
  • Referans19 Kumar, S., Kaur, G., (2013), Detection of Heart Diseases using Fuzzy Logic, International Journal of Engineering Trends and Technology (IJETT) , Volume 4, Issue 6, 2694-2699
  • Referans20 Lee, C,S., & Wang, M.H., (2011), A fuzzy expert system for diabetes decision support application, IEEE Transactions on Systems, Man, and Cybernetics, 41(1), 139-153
  • Referans21 Lu, L., Liu, M., Sun, R., Zheng, Y., Zhang, P., (2015), Myocardial Infarction: Symptoms and Treatments, Cell Biochem Biophys 72, 865–867
  • Referans22 Malmir, B., Amini, M., Chang, S.I., (2017), A medical decision support system for disease diagnosis under uncertainty, Expert Systems With Applications 88, 95-108
  • Referans23 Mamdani, E.H., Assilian, S., (1975), An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, Volume 7, Issue 1, 1-13
  • Referans24 Nilashi, M., Ibrahım, O., Ahmadi, H., Shahmoradi, L., (2017), A knowledge-based system for breast cancer classification using fuzzy logic method, Telematics and Informatics, Volume 34, Issue 4, 133-144
  • Referans25 Onat, A., (2001), Risk factors and cardiovascular disease in Turkey, Atherosclerosis, 156, 1-10.
  • Referans26 Palaniappan, S., Awang, R., (2008), Intelligent heart disease prediction system using data mining techniques, International Journal of Computer Science and Network Security, 8 (8), 108–115.
  • Referans27 Patil, S.B., Kumaraswamy, Y.S., (2009), Intelligent and effective heart attack prediction system using data mining and artificial neural network, European Journal of Scientific Research, 31 (4), 642–656.
  • Referans28 Phuong, N.H., Kreinovich, V., (2001), Fuzzy logic and its applications in medicine, International Journal of Medical Informatics, Volume 62, Issues 2-3, 165-173
  • Referans29 Ross, T.J, (2016), Fuzzy Logic with Engineering Applications, New York: Wiley-Blackwell.
  • Referans 30 Rustempasic, I., & Can, M. (2013), Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition, Southeast Europe Journal of Soft Computing, 2(1), 42–49.
  • Referans31 Sağlık Bakanlığı (2010), Türkiye kalp ve damar hastalıklarını önleme ve kontrol programı. Birincil, ikincil, üçüncül korumaya yönelik stratejik plan ve eylem planı (2010-2014), T.C. Sağlık Bakanlığı, Temel Sağlık Hizmetleri Genel Müdürlüğü. Yayın No: 812. Ankara. Anıl Matbaası. (4-30).
  • Referans32 Saikia, D., & Dutta, J.C., (2016), Early diagnosis of dengue disease using fuzzy inference system, in Proceedings of 2016 international conference on microelectronics, computing and communications (MicroCom) pp. 1–6. IEEE.
  • Referans33 Samuel, O. W., Omisore, M. O., &Ojokoh, B.A.(2013), A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever, Expert Systems with Applications, 40(10), 4164–4171.
  • Referans34 Sarı, M., Murat, Y., Kırabalı, M., (2005), Bulanık Modelleme Yaklaşımı Ve Uygulamaları, Journal of Science and Technology of Dumlupınar University , (009) , 77-92
  • Referans35 Syropoulos, A., Grammenos, T., (2020), A Modern Introduction to Fuzzy Mathematics, New York: Wiley
  • Referans36 Şahinler, S., Görgülü, Ö.,Bek, Yüksel., (2006), Sağlık Alanında Bulanık Mantık Yöntemlerinin Uygulanabilirliği ,IX. Ulusal Biyoistatistik Kongresi, Zonguldak 2006
  • Referans37 T.C. Sağlık Bakanlığı, Türkiye Kalp ve Damar Hastalıkları Önleme ve Kontrol Programı Eylem Planı (2015-2020), erişim tarihi: 9 Ekim 2020 https://tkd.org.tr/TKDData/Uploads/files/Turkiye-kalp-ve-damar-hastaliklari-onleme-ve-kontrol-programi.pdf
  • Referans38 Thakur, S., Raw, S.N., & Sharma, R., (2016), Design of a fuzzy model for thalassemia disease diagnosis: Using mamdani type fuzzy inference system, International Journal of Pharmacy and Pharmaceutical Sciences, 8(4), 356-361.
  • Referans39 Torun, S., (2007), Koroner Kalp Hastalığı Riski Tanısı Ve Tedavisi İçin Hiyerarşik Bir Bulanık Uzman Sistem Tasarımı, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi
  • Referans40 World Health Organization, (2017), Cardiovascular diseases (CVDs): key facts. Erişim tarihi: 9 Ekim 2020 https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
  • Referans41 World Health Organization, (2020), Tobacco responsible for 20% of deaths from coronary heart disease, erişim tarihi: 10 Ekim 2020 https://www.who.int/news/detail/22-09-2020-tobacco-responsible-for-20-of-deaths-from-coronary-heart-disease
  • Referans42 Zadeh, L.A., (1965), Fuzzy Sets, Information and Control, Volume 8, Issue 3, 338-353

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Seher ARSLANKAYA (Sorumlu Yazar)
Sakarya University
0000-0001-6023-2901
Türkiye


Miraç Tuba ÇELİK
SAKARYA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0002-0298-2170
Türkiye

Yayımlanma Tarihi 31 Mart 2021
Başvuru Tarihi 8 Aralık 2020
Kabul Tarihi 18 Mart 2021
Yayınlandığı Sayı Yıl 2021, Cilt 7, Sayı 1

Kaynak Göster

Bibtex @araştırma makalesi { ijcesen837731, journal = {International Journal of Computational and Experimental Science and Engineering}, issn = {}, eissn = {2149-9144}, address = {}, publisher = {İskender AKKURT}, year = {2021}, volume = {7}, pages = {1 - 8}, doi = {10.22399/ijcesen.837731}, title = {Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks}, key = {cite}, author = {Arslankaya, Seher and Çelik, Miraç Tuba} }
APA Arslankaya, S. & Çelik, M. T. (2021). Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks . International Journal of Computational and Experimental Science and Engineering , 7 (1) , 1-8 . DOI: 10.22399/ijcesen.837731
MLA Arslankaya, S. , Çelik, M. T. "Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks" . International Journal of Computational and Experimental Science and Engineering 7 (2021 ): 1-8 <https://dergipark.org.tr/tr/pub/ijcesen/issue/57886/837731>
Chicago Arslankaya, S. , Çelik, M. T. "Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks". International Journal of Computational and Experimental Science and Engineering 7 (2021 ): 1-8
RIS TY - JOUR T1 - Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks AU - Seher Arslankaya , Miraç Tuba Çelik Y1 - 2021 PY - 2021 N1 - doi: 10.22399/ijcesen.837731 DO - 10.22399/ijcesen.837731 T2 - International Journal of Computational and Experimental Science and Engineering JF - Journal JO - JOR SP - 1 EP - 8 VL - 7 IS - 1 SN - -2149-9144 M3 - doi: 10.22399/ijcesen.837731 UR - https://doi.org/10.22399/ijcesen.837731 Y2 - 2021 ER -
EndNote %0 International Journal of Computational and Experimental Science and Engineering Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks %A Seher Arslankaya , Miraç Tuba Çelik %T Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks %D 2021 %J International Journal of Computational and Experimental Science and Engineering %P -2149-9144 %V 7 %N 1 %R doi: 10.22399/ijcesen.837731 %U 10.22399/ijcesen.837731
ISNAD Arslankaya, Seher , Çelik, Miraç Tuba . "Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks". International Journal of Computational and Experimental Science and Engineering 7 / 1 (Mart 2021): 1-8 . https://doi.org/10.22399/ijcesen.837731
AMA Arslankaya S. , Çelik M. T. Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks. IJCESEN. 2021; 7(1): 1-8.
Vancouver Arslankaya S. , Çelik M. T. Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks. International Journal of Computational and Experimental Science and Engineering. 2021; 7(1): 1-8.
IEEE S. Arslankaya ve M. T. Çelik , "Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks", International Journal of Computational and Experimental Science and Engineering, c. 7, sayı. 1, ss. 1-8, Mar. 2021, doi:10.22399/ijcesen.837731

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