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Koroner Arter Hastalığı Riskinin Veri Madenciliği Yöntemleri İle İncelenmesi

Year 2018, Volume: 10 Issue: 1, 85 - 93, 29.01.2017
https://doi.org/10.29137/umagd.419663

Abstract

Günümüzde
Kardiyovasküler Hastalıklar oldukça yaygındır ve ölüm nedenlerinin başında
gelmektedir. Kardiyovasküler Hastalıkların bir tipi olan Koroner Arter
Hastalığının doğru ve zamanında teşhisi çok önemlidir. Koroner arter
hastalığının kesin tanısı ve hastalık şiddetinin saptanmasında invaziv bir
yöntem olan anjiyografi altın standart olarak kullanılmaktadır. Anjiyografi,
maliyeti yüksek ve ileri seviyede uzmanlık gerektiren bir yöntem olmasının
yanında ciddi komplikasyonlara da sebep olabilmektedir. Bu nedenlerle daha ucuz
ve etkili bir yaklaşım sağlayabilecek olan veri madenciliğinin kullanımı üzerinde
çalışmalar yapılmaktadır. Bu çalışmada Koroner Arter Hastalığı riskinin
tespitinde bir sınıflama modeli geliştirmek için veri madenciliği yaklaşımı
uygulanmıştır. Çalışma kapsamında sınıflandırma yöntemleri ile elde edilen
sonuçlar ve doğru sınıflandırma oranları karşılaştırılmıştır. Bunun için
Cleveland kliniğine ait, 303 kayıt ve 14 değişken içeren kalp hastalığı veri
kümesi kullanılmıştır. Gerekli hesaplamalar ve modelleri elde etmek için Weka
paket programında 1R, J48 Karar Ağacı, Naive Bayes ve Çok katmanlı yapay sinir
ağı (YSA) sınıflandırma yöntemleri uygulanmıştır. Uygulama sonucunda Koroner
Arter Hastalığının tespitinde en iyi sonucun %83,498 doğruluk oranı ile Çok
katmanlı YSA sınıflandırma yöntemi ile elde edildiği görülmüştür. Çok katmanlı YSA
algoritmasını Naive Bayes ve Düzenlenmiş J48 Karar Ağacı algoritmaları
izlemiştir.

References

  • Abdullah, A. S. (2012). A Data Mining Model to Predict and Analyze the Events Related to Coronary Heart Disease using Decision Trees with Particle Swarm Optimization for Feature Selection. International Journal of Computer Applications, 55(8).
  • Alizadehsani, R., Habibi, J., Hosseini, M. J., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., Sani, Z. A. (2013). A Data Mining Approach for Diagnosis of Coronary Artery Disease. Computer Methods and Programs in Biomedicine, 111(1), 52-61.
  • Alizadehsani, R., Hosseini, M. J., Sani, Z. A., Ghandeharioun, A., & Boghrati, R. (2012). Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms. In Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on (pp. 9-16). IEEE.
  • Anbarasi, M., Anupriya, E., & Iyengar, N. C. S. N. (2010). Enhanced Prediction of Heart Disease with Feature Subset Selection Using Genetic Algorithm. International Journal of Engineering Science and Technology, 2(10), 5370-5376.
  • Avşar, A., Önder, Akçı., Beyter, M. E. (2011). Aterosklerozun Patogenezi (Aterogenez). Turkiye Klinikleri Journal of Cardiology Special Topics, 4(2), 1-15.
  • Cardiovascular diseases (CVDs), http://www.who.int/mediacentre/factsheets/fs317/en/ (Erişim tarihi; Ekim, 2016).
  • Ceylan, Y., Kaya, Y., & Tuncer, M. (2011). Akut Koroner Sendrom Kliniği ile Başvuran Hastalarda Koroner Arter Hastalığı Risk Faktörleri. Van Tıp Dergisi, 18(3), 147-54.
  • Chen, A. H., Huang, S. Y., Hong, P. S., Cheng, C. H., & Lin, E. J. (2011, September). HDPS: Heart Disease Prediction System. In Computing in Cardiology, 2011 (pp. 557-560). IEEE.
  • Çınar, H. ve Arslan, G., 2008. “Veri madenciliği ve CRISP-DM yaklaşımı”, XVII. İstatistik Araştırma Sempozyumu, 304-314, Ankara.
  • De Flines, J., & Scheen, A. J. (2009). Management Of Metabolic Syndrome And Associated Cardiovascular Risk Factors. Acta Gastro-Enterologica Belgica, 73(2), 261-266.
  • El-Bialy, R., Salamay, M. A., Karam, O. H., & Khalifa, M. E. (2015). Feature Analysis of Coronary Artery Heart Disease Data Sets. Procedia Computer Science, 65, 459-468.
  • Erdoğan, N., Altın, L., Altunkan, Ş. (2002). Elektron Beam Tomografi ile Koroner Arterlerdeki Kalsiyum Miktar›n›n Saptanması. Tanısal ve Girişimsel Radyoloji, 8, 533-537.
  • Griffin, B. P., Callahan T.D., Menon, V.(Eds.). (2012). Manual of Cardiovascular Medicine. Lippincott Williams & Wilkins.
  • Mann, D. L., Zipes, D. P., Libby, P., & Bonow, R. O. (2014). Braunwald's Heart Disease: a Textbook of Cardiovascular Medicine. Elsevier Health Sciences.
  • Ökçün, B., Gürmen, T. (2007). Koroner Anjiyografi Komplikasyonları ve Tedavisi. Turkiye Klinikleri Journal of Internal Medical Sciences, 3(42), 48-72.
  • Palaniappan, S., & Awang, R. (2008). Intelligent Heart Disease Prediction System Using Data Mining Techniques. In Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on (pp. 108-115). IEEE.
  • Pandey, A. K., Pandey, P., & Jaiswal, K. L. (2013). A Heart Disease Prediction Model Using Decision Tree. IUP Journal of Computer Sciences, 7(3), 43.
  • Shafique, U., Majeed, F., Qaiser, H., & Mustafa, I. U. (2015). Data Mining in Healthcare for Heart Diseases. International Journal of Innovation and Applied Studies, 10(4), 1312.
  • Sharan M.L, Sathees, K.B. (2016). Analysis of Cardiovascular Heart Disease Prediction Using Data Mining Techniques. Analysis, 4(1), 55-58.
  • Soni, J., Ansari, U., Sharma, D., Soni, S. (2011). Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. International Journal of Computer Applications, 17(8), 43-48.
  • Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255.
  • Onat, A., Sansoy, V., Soydan, İ., Tokgözoğlu, L., & Adalet, K. (2003). TEKHARF, Oniki Yıllık İzleme Deneyimine Göre Türk Erişkinlerinde Kalp Sağlığı. İstanbul Türkiye, 12-4.
  • Verma, L., Srivastava, S., Negi, P. C. (2016). A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data. Journal of Medical Systems, 40(7), 1-7.
  • Wirth, R., & Hipp, J. (2000). CRISP-DM: Towards a Standard Process Model for Data Mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 29-39.
  • Wong, N. D. (2014). Epidemiological Studies of CHD and the Evolution of Preventive Cardiology. Nature Reviews. Cardiology, 11(5), 276.

Identification of Coronary Artery Disease Risk Using Data Mining Techniques

Year 2018, Volume: 10 Issue: 1, 85 - 93, 29.01.2017
https://doi.org/10.29137/umagd.419663

Abstract

Cardiovascular
Diseases are quite common nowadays and are one of the leading causes of death.
The correct and timely diagnosis of Coronary Artery Disease, a type of
Cardiovascular Disease, is very important for further treatment of the
patients. For accurate diagnosis of coronary artery disease and determination
of disease severity, angiography, which is an invasive and gold standard
diagnosis tool, is used. Angiography is a costly and advanced method that
requires clinical expertise and may cause serious complications. For these
reasons, research on using data mining techniques, which is a cheaper and more
effective approach, for diagnosis is one of today's research topics. In this
study, classification-based data mining methods were used to determine the risk
of coronary artery disease and these methods were compared in terms of
accuracy. A data set consisting of 303 patient records and 14 attributes of
Cleveland clinic were used. In particular, 1R, J48 Decision Tree, Naive Bayes
and Multilayer Artificial Neural Network classification methods were applied on
this data set with the help of WEKA program. The best result (in terms of
correct diagnosis ratio) in determining risk of Coronary Artery Disease was
obtained with Artificial Neural Network classification method with an accuracy
of 83.498%. The multi-layer ANN algorithm was followed by Naive Bayes and the
J48 Decision Tree algorithms

References

  • Abdullah, A. S. (2012). A Data Mining Model to Predict and Analyze the Events Related to Coronary Heart Disease using Decision Trees with Particle Swarm Optimization for Feature Selection. International Journal of Computer Applications, 55(8).
  • Alizadehsani, R., Habibi, J., Hosseini, M. J., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., Sani, Z. A. (2013). A Data Mining Approach for Diagnosis of Coronary Artery Disease. Computer Methods and Programs in Biomedicine, 111(1), 52-61.
  • Alizadehsani, R., Hosseini, M. J., Sani, Z. A., Ghandeharioun, A., & Boghrati, R. (2012). Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms. In Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on (pp. 9-16). IEEE.
  • Anbarasi, M., Anupriya, E., & Iyengar, N. C. S. N. (2010). Enhanced Prediction of Heart Disease with Feature Subset Selection Using Genetic Algorithm. International Journal of Engineering Science and Technology, 2(10), 5370-5376.
  • Avşar, A., Önder, Akçı., Beyter, M. E. (2011). Aterosklerozun Patogenezi (Aterogenez). Turkiye Klinikleri Journal of Cardiology Special Topics, 4(2), 1-15.
  • Cardiovascular diseases (CVDs), http://www.who.int/mediacentre/factsheets/fs317/en/ (Erişim tarihi; Ekim, 2016).
  • Ceylan, Y., Kaya, Y., & Tuncer, M. (2011). Akut Koroner Sendrom Kliniği ile Başvuran Hastalarda Koroner Arter Hastalığı Risk Faktörleri. Van Tıp Dergisi, 18(3), 147-54.
  • Chen, A. H., Huang, S. Y., Hong, P. S., Cheng, C. H., & Lin, E. J. (2011, September). HDPS: Heart Disease Prediction System. In Computing in Cardiology, 2011 (pp. 557-560). IEEE.
  • Çınar, H. ve Arslan, G., 2008. “Veri madenciliği ve CRISP-DM yaklaşımı”, XVII. İstatistik Araştırma Sempozyumu, 304-314, Ankara.
  • De Flines, J., & Scheen, A. J. (2009). Management Of Metabolic Syndrome And Associated Cardiovascular Risk Factors. Acta Gastro-Enterologica Belgica, 73(2), 261-266.
  • El-Bialy, R., Salamay, M. A., Karam, O. H., & Khalifa, M. E. (2015). Feature Analysis of Coronary Artery Heart Disease Data Sets. Procedia Computer Science, 65, 459-468.
  • Erdoğan, N., Altın, L., Altunkan, Ş. (2002). Elektron Beam Tomografi ile Koroner Arterlerdeki Kalsiyum Miktar›n›n Saptanması. Tanısal ve Girişimsel Radyoloji, 8, 533-537.
  • Griffin, B. P., Callahan T.D., Menon, V.(Eds.). (2012). Manual of Cardiovascular Medicine. Lippincott Williams & Wilkins.
  • Mann, D. L., Zipes, D. P., Libby, P., & Bonow, R. O. (2014). Braunwald's Heart Disease: a Textbook of Cardiovascular Medicine. Elsevier Health Sciences.
  • Ökçün, B., Gürmen, T. (2007). Koroner Anjiyografi Komplikasyonları ve Tedavisi. Turkiye Klinikleri Journal of Internal Medical Sciences, 3(42), 48-72.
  • Palaniappan, S., & Awang, R. (2008). Intelligent Heart Disease Prediction System Using Data Mining Techniques. In Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on (pp. 108-115). IEEE.
  • Pandey, A. K., Pandey, P., & Jaiswal, K. L. (2013). A Heart Disease Prediction Model Using Decision Tree. IUP Journal of Computer Sciences, 7(3), 43.
  • Shafique, U., Majeed, F., Qaiser, H., & Mustafa, I. U. (2015). Data Mining in Healthcare for Heart Diseases. International Journal of Innovation and Applied Studies, 10(4), 1312.
  • Sharan M.L, Sathees, K.B. (2016). Analysis of Cardiovascular Heart Disease Prediction Using Data Mining Techniques. Analysis, 4(1), 55-58.
  • Soni, J., Ansari, U., Sharma, D., Soni, S. (2011). Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. International Journal of Computer Applications, 17(8), 43-48.
  • Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255.
  • Onat, A., Sansoy, V., Soydan, İ., Tokgözoğlu, L., & Adalet, K. (2003). TEKHARF, Oniki Yıllık İzleme Deneyimine Göre Türk Erişkinlerinde Kalp Sağlığı. İstanbul Türkiye, 12-4.
  • Verma, L., Srivastava, S., Negi, P. C. (2016). A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data. Journal of Medical Systems, 40(7), 1-7.
  • Wirth, R., & Hipp, J. (2000). CRISP-DM: Towards a Standard Process Model for Data Mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 29-39.
  • Wong, N. D. (2014). Epidemiological Studies of CHD and the Evolution of Preventive Cardiology. Nature Reviews. Cardiology, 11(5), 276.
There are 25 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Şeyma Cihan This is me

Bergen Karabulut

Güvenç Arslan This is me

Gökhan Cihan This is me

Publication Date January 29, 2017
Submission Date February 10, 2017
Published in Issue Year 2018 Volume: 10 Issue: 1

Cite

APA Cihan, Ş., Karabulut, B., Arslan, G., Cihan, G. (2017). Koroner Arter Hastalığı Riskinin Veri Madenciliği Yöntemleri İle İncelenmesi. International Journal of Engineering Research and Development, 10(1), 85-93. https://doi.org/10.29137/umagd.419663

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