Araştırma Makalesi
Yıl 2019,
Cilt: 4 Sayı: 2, 74 - 79, 01.12.2019
Öz
Kaynakça
- 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)
Yıl 2019,
Cilt: 4 Sayı: 2, 74 - 79, 01.12.2019
Öz
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.
Anahtar Kelimeler
yapay sinir ağları destek vektör makinası kalp rahatsızlığı tahmi
Kaynakça
- 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)
Toplam 16 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | PAPERS |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Aralık 2019 |
| Gönderilme Tarihi | 6 Ocak 2019 |
| Kabul Tarihi | 6 Mart 2019 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 4 Sayı: 2 |
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