Destek Vektör Makinasi Algoritması ile Kalp Hastalıklarının Tahmini

İrem Ozcan; Beyda Tasar; Ahmet Burak Tatar; Oguz Yakut

Research Article

Destek Vektör Makinasi Algoritması ile Kalp Hastalıklarının Tahmini

Year 2019, Volume: 4 Issue: 2, 74 - 79, 01.12.2019

İrem Ozcan Beyda Tasar Ahmet Burak Tatar Oguz Yakut

Abstract

References

Bajaj, P., Choudhary, K. and Chauhan, R. (2015) Prediction of Occurrence of Heart Disease and Its Dependability on RCT Using Data Mining Techniques. Advances in Intelligent Systems and Computing, Springer India, 340, 851-858.
Beheshti, Z., Shamsuddin, S.M., Beheshti, E. and Yuhaniz, S.S. (2014) Enhancement of Artificial Neural Network Learning Using Centripetal Accelerated Particle Swarm Optimization for Medical Diseases Diagnosis. Soft Computing, 18, 2253-2270. http://dx.doi.org/10.1007/s00500-013-1198-0
Das, R., Turkoglu, I. and Sengur, A. (2009) Effective Diagnosis of Heart Disease through Neural Networks Ensembles. Expert Systems with Applications, 36, 7675-7680. http://dx.doi.org/10.1016/j.eswa.2008.09.013
Howard D., Mark B., (2014). Neural Network Toolbox For Use with MATLAB®,Issue-6, The MathWorks, Inc.3 Apple Hill Drive Natick, MA 01760-2098Global Action Plan for The Prevention and Control of NCDs 2013-2020 WHO 2013. http://www. who.int/ nmh/publications/ncd-action-plan/en/ (Global Action Plan 2013-2020) (Erişim Haziran 2014)
Global Status Report on Noncommunicable Diseases 2014, WHO, http://apps.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf?ua=1 (Erişim tarihi Mayıs 2015)
Kahramanli, H. and Allahverdi, N.(2008) Design of a Hybrid System for the Diabetes and Heart Diseases. Expert Systems with Applications, 35, 82-89. http://dx.doi.org/10.1016/j.eswa.2007.06.004
Kumari, M. and Godara, S. (2011) Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction 1. International Journal of Computer Science and Technology, 2, 304-308.
Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A. and Tourassi, G.D. (2008) Training Neural Network Classifiers for Medical Decision Making: The Effects of Imbalanced Datasets on Classification Performance. Neural Networks, 21, 427-436. http://dx.doi.org/10.1016/j.neunet.2007.12.031
Pal, M., Foody, G.M., (2010), Feature selection for classification of hyperspectral data by SVM, IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2297–2307.
Peker, M. , (2016) A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM, Journal of Medical Systems, 40: 116. doi:10.1007/s10916-016-0477-6
Purwar, A. and Singh, S.K. (2015) Hybrid Prediction Model with Missing Value Imputation for Medical Data. Expert Systems with Applications, 42, 5621-5631. http://dx.doi.org/10.1016/j.eswa.2015.02.050
Shao, Y.E., Hou, C.-D. and Chiu, C.-C. (2014) Hybrid Intelligent Modeling Schemes for Heart Disease Classification. Applied Soft Computing, 14, 47-52. http://dx.doi.org/10.1016/j.asoc.2013.09.020
Turabieh, H. (2016) A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease. American Journal of Operations Research, 6, 136-146. http://dx.doi.org/10.4236/ajor.2016.62016
T.C. Sağlık Bakanlığı, Stratejik Plan 2013-2014 (Basım 2012)
T.C. Sağlık Bakanlığı Temel Sağlık Hizmetleri Genel Müdürlüğü, Türkiye Kalp ve Damar Hastalıklarını Önleme ve Kontrol Programı 2010-2014, Basım 2010 Ankara.
UCI, Machine Learning Repository, Statlog (Heart) Data Set, http://archive.ics.uci.edu/ml/datasets/statlog+(heart)

Destek Vektör Makinasi Algoritması ile Kalp Hastalıklarının Tahmini

Year 2019, Volume: 4 Issue: 2, 74 - 79, 01.12.2019

İrem Ozcan Beyda Tasar Ahmet Burak Tatar Oguz Yakut

Abstract

Son yıllarda yapılan
araştırmalar ve istatistikler ani yaşam kayıplarının yüzde 46,2’si (17,5
milyon) kalp ve damar hastalıkları nedeni yaşandığını göstermektedir. Bu
nedenle erken teşhis ve müdahalenin önemli olduğu bu tür hastalıkların basit ve
kolay bir takım testler ile hastanın rutin kontrolü son derece önemlidir. Bu
çalışmada destek vektör makinası ve ileri yayılımlı YSA tahmin algoritmalarının
kalp hastalığının erken teşhisinde kullanılabilir olduğu önerilmiş ve
kullanılmıştır. 170 adet kişiden alınan veriler kişisel ve tıbbi veriler kullanılarak
yapılan çalışmada İleri yayılımlı YSA ile
% 83.33, destek vektör makinası algoritması ile de % 91.67’lik bir
doğrulukta kalp hastalığının varlığının tespiti başarılmıştır.

Keywords

yapay sinir ağları, destek vektör makinası, kalp rahatsızlığı tahmi

References

Bajaj, P., Choudhary, K. and Chauhan, R. (2015) Prediction of Occurrence of Heart Disease and Its Dependability on RCT Using Data Mining Techniques. Advances in Intelligent Systems and Computing, Springer India, 340, 851-858.
Beheshti, Z., Shamsuddin, S.M., Beheshti, E. and Yuhaniz, S.S. (2014) Enhancement of Artificial Neural Network Learning Using Centripetal Accelerated Particle Swarm Optimization for Medical Diseases Diagnosis. Soft Computing, 18, 2253-2270. http://dx.doi.org/10.1007/s00500-013-1198-0
Das, R., Turkoglu, I. and Sengur, A. (2009) Effective Diagnosis of Heart Disease through Neural Networks Ensembles. Expert Systems with Applications, 36, 7675-7680. http://dx.doi.org/10.1016/j.eswa.2008.09.013
Howard D., Mark B., (2014). Neural Network Toolbox For Use with MATLAB®,Issue-6, The MathWorks, Inc.3 Apple Hill Drive Natick, MA 01760-2098Global Action Plan for The Prevention and Control of NCDs 2013-2020 WHO 2013. http://www. who.int/ nmh/publications/ncd-action-plan/en/ (Global Action Plan 2013-2020) (Erişim Haziran 2014)
Global Status Report on Noncommunicable Diseases 2014, WHO, http://apps.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf?ua=1 (Erişim tarihi Mayıs 2015)
Kahramanli, H. and Allahverdi, N.(2008) Design of a Hybrid System for the Diabetes and Heart Diseases. Expert Systems with Applications, 35, 82-89. http://dx.doi.org/10.1016/j.eswa.2007.06.004
Kumari, M. and Godara, S. (2011) Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction 1. International Journal of Computer Science and Technology, 2, 304-308.
Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A. and Tourassi, G.D. (2008) Training Neural Network Classifiers for Medical Decision Making: The Effects of Imbalanced Datasets on Classification Performance. Neural Networks, 21, 427-436. http://dx.doi.org/10.1016/j.neunet.2007.12.031
Pal, M., Foody, G.M., (2010), Feature selection for classification of hyperspectral data by SVM, IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2297–2307.
Peker, M. , (2016) A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM, Journal of Medical Systems, 40: 116. doi:10.1007/s10916-016-0477-6
Purwar, A. and Singh, S.K. (2015) Hybrid Prediction Model with Missing Value Imputation for Medical Data. Expert Systems with Applications, 42, 5621-5631. http://dx.doi.org/10.1016/j.eswa.2015.02.050
Shao, Y.E., Hou, C.-D. and Chiu, C.-C. (2014) Hybrid Intelligent Modeling Schemes for Heart Disease Classification. Applied Soft Computing, 14, 47-52. http://dx.doi.org/10.1016/j.asoc.2013.09.020
Turabieh, H. (2016) A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease. American Journal of Operations Research, 6, 136-146. http://dx.doi.org/10.4236/ajor.2016.62016
T.C. Sağlık Bakanlığı, Stratejik Plan 2013-2014 (Basım 2012)
T.C. Sağlık Bakanlığı Temel Sağlık Hizmetleri Genel Müdürlüğü, Türkiye Kalp ve Damar Hastalıklarını Önleme ve Kontrol Programı 2010-2014, Basım 2010 Ankara.
UCI, Machine Learning Repository, Statlog (Heart) Data Set, http://archive.ics.uci.edu/ml/datasets/statlog+(heart)

There are 16 citations in total.

Details

Primary Language	Turkish
Journal Section	PAPERS
Authors	İrem Ozcan Beyda Tasar Ahmet Burak Tatar Oguz Yakut
Publication Date	December 1, 2019
Submission Date	January 6, 2019
Acceptance Date	March 6, 2019
Published in Issue	Year 2019 Volume: 4 Issue: 2

Cite

APA	Ozcan, İ., Tasar, B., Tatar, A. B., Yakut, O. (2019). Destek Vektör Makinasi Algoritması ile Kalp Hastalıklarının Tahmini. Computer Science, 4(2), 74-79.