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Hematolojik Hastalıkların Tanısında Klonal Seçim Algortiması ile birlikte kullanılan K En yakın Komşu ve Regresyon Ağacı Sınıflandırıcılarının Mukayesesi

Yıl 2014, Cilt: 5 Sayı: 16, 7 - 20, 01.07.2014
https://doi.org/10.5824/1309-1581.2014.3.001.x

Öz

Bu çalışmanın amacı Hematolojik parametrelere bağlı sınıflandırma performansını artıracak bir yöntem geliştirmektir. Sınıflandırma problemlerinde Klonal Seçim Algoritması ile birlikte genellikle kNN sınıflandırıcısının kullanıldığı görülmüştür. Bu çalışmada diğer çalışmalardan farklı olarak kNN sınıflandırma algoritmasının yerine Gini algoritması uygulanmıştır ve daha yüksek başarı elde edilmiştir. Dünya sağlık örgütünün verilerine göre dünyadaki kadınların yaklaşık %10'u anemidir. Anemi hayat kalitesini düşüren ve tedavi edilmediğinde ciddi etkileri olan bir hastalıktır. Demir eksikliği anemisi aneminin en yaygın tipidir ve kadınlar erkeklere oranla bu hastalıktan daha fazla etkilenmektedir. Bu nedenle bu çalışmada örnek uygulama için anemi tercih edilmiştir. Hematolojik parametrelere bakarak tanı koyulan diğer hastalıklarda da önerilen metodun başarılı sonuçlar üreteceği beklenmektedir. Çalışmanın sonunda farklı yöntemlerle elde edilen başarı oranları ROC analizi yöntemiyle karşılaştırılmıştır. Bellek tabanlı sınıflandırıcı ile doğruluk oranı %96 olarak bulunurken Regresyon ağacı yöntemiyle doğruluk oranı %98.73 elde edilmiştir. Klonal Seçim Algoritmasında sınıflandırıcı olarak kNN yerine Gini algoritmasının kullanılması ile, Yapay Sinir Ağları metodlarından da daha yüksek başarı elde edilmiştir.

Kaynakça

  • Bozkurt, M. R., Yurtay, N., Yilmaz, Z., & Sertkaya, C. (2014). Comparison of different methods for determining diabetes. Turkish Journal of Electrical Engineering & Computer Sciences,22(4), 1044–1055. doi:10.3906/elk-1209-82
  • De Castro, L. N., & Timmis, J. (2002). Artificial Immune Systems: A Novel Paradigm to Pattern Recognition. InUniversity of Paisley (pp. 67–84). Springer Verlag, University of Paisley, UK.
  • De Castro, L. N., & Von Zuben, F. J. (2002). Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation,6(3), 239–251. doi:10.1109/TEVC.2002.1011539
  • De Castro, L. N., & Von Zuben, F. J. (2002). The Clonal Selection Algorithm with Engineering Applications. InIn GECCO 2002 - Workshop Proceedings (pp. 36–37). Morgan Kaufmann.
  • De Castro, L. N. & Von Zuben, F. J. (1999). Artificial Immune Systems: Part I-Basic Theory and Applications. Technical Report, TR-DCA 01/99.
  • Er, O., Yumusak, N., & Temurtas, F. (2012). Diagnosis of chest diseases using artificial immune system. Expert Systems with Applications, 39(2), 1862–1868. doi:10.1016/j.eswa.2011.08.064
  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters (vol 27, pp 861-874).
  • Garrett, S. M. (2005). How Do We Evaluate Artificial Immune Systems? Evol. Comput., 13(2), 145–177. doi:10.1162/1063656054088512
  • Hart, E., Ross, P., & Nelson, J. (1998). Producing robust schedules via an artificial immune system. IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (pp. 464–469). doi:10.1109/ICEC.1998.699852
  • Hillman, R. S., Ault, K. A., Leporrier, M. & Rinder, H. M. (2012). Hematology in Clinical Practice. McGraw-Hill Publisher.
  • Jun, J.-H., Lee, D.-W., & Sim, K.-B. (1999). Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system. IEEE International Conference on Systems, Man, and Cybernetics. IEEE SMC ’99 Conference Proceedings (Vol. 6, pp. 614–619). doi:10.1109/ICSMC.1999.816622
  • Kihel, B. K., & Benyettou, M. (2011). Parkinson’s Disease Recognition Using Artificial Immune System. Journal of Software Engineering and Applications, 04(07), 391–395. doi:10.4236/jsea.2011.47045
  • Kodaz, H., Özşen, S., Arslan, A., & Güneş, S. (2009). Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease. Expert Systems with Applications, 36(2, Part 2), 3086–3092. doi:10.1016/j.eswa.2008.01.026
  • Latifolu, F., Kodaz, H., Kara, S., & Güneş, S. (2007). Medical application of Artificial Immune Recognition System (AIRS): Diagnosis of atherosclerosis from carotid artery Doppler signals. Computers in Biology and Medicine,37(8), 1092–1099. doi:10.1016/j.compbiomed.2006.09.00
  • Masala, G. L., Golosio, B., Cutzu, R., & Pola, R. (2013). A two-layered classifier based on the radial basis function for the screening of thalassaemia. Computers in Biology and Medicine,43(11), 1724–1731. doi:10.1016/j.compbiomed.2013.08.020
  • MATLAB® Documentation Neural Network Toolbox Help. (2014). Feedforward neural network, Release 2014a, The MathWorks Inc.
  • MATLAB® Documentation Neural Network Toolbox Help. (2014). Probabilistic neural networks, Release 2014a, The MathWorks Inc.
  • MedCalc for Windows, version 12.5 MedCalc Software, Ostend, Belgium.
  • Mohammad, A. H., & Zitar, R. A. (2011). Application of genetic optimized artificial immune system and neural networks in spam detection. Applied Soft Computing, 11(4), 3827–3845. doi:10.1016/j.asoc.2011.02.021
  • Mori, K., Tsukiyama, M., & Fukuda, T. (1997). Artificial immunity based management system for a semiconductor production line. IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation (Vol. 1, pp. 851–855 vol.1). doi:10.1109/ICSMC.1997.626207
  • Nasraoui, O., Dasgupta, D., & Gonzalez, F. (2002). The Promise and Challenges of Artificial Immune System Based Web Usage Mining. Workshop on Web Analytics at Second SIAM, International Conference on Data mining (SDM). Arlington.
  • Özaslan, E. & Delibaşı, T. 2012. Tusem Dahiliye. Ankara: Tusem Publisher.
  • Özkan, Y. (2013). Veri Madenciliği Yöntemleri (2nd ed.). Istanbul: Papatya Publisher.
  • Polat, K., Şahan, S., & Güneş, S. (2007). A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis. Expert Systems with Applications, 32(4), 1141–1147. doi:10.1016/j.eswa.2006.02.007
  • Sengur, A. (2008). An expert system based on principal component analysis, artificial immune system and fuzzy -NN for diagnosis of valvular heart diseases. Computers in Biology and Medicine, 38(3), 329–338. doi:10.1016/j.compbiomed.2007.11.004
  • Sobh, T. S., & Mostafa, W. M. (2011). A cooperative immunological approach for detecting network anomaly. Applied Soft Computing,11(1), 1275–1283. doi:10.1016/j.asoc.2010.03.004
  • Şahan, S., Polat, K., Kodaz, H., & Güneş, S. (2007). A new hybrid method based on fuzzy-artificial immune system and -nn algorithm for breast cancer diagnosis. Computers in Biology and Medicine, 37(3), 415–423. doi:10.1016/j.compbiomed.2006.05.003
  • Tjornfelt-Jensen, M., & Hansen, T. K. (1999). Robust solutions to job shop problems. In Proceedings of the 1999 Congress on Evolutionary Computation. CEC 99 (Vol. 2, p. -1144 Vol. 2). doi:10.1109/CEC.1999.782551
  • Turkish Statistical Institute. (2013). Health Survey 2012. Ankara: Printing Division.
  • Vos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., … Murray, C. J. (2012).
  • Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2163– 2196. doi:10.1016/S0140-6736(12)61729-2
  • Weinstein, S., Obuchowski, N. A. & Lieber, M. L.(2005). Fundamentals of Clinical Research for Radiologists. American Journal of Roentgenology (vol 184 , pp 14 -19).

Comparison of K Nearest Neighbours And Regression Tree Classifiers Used With Clonal Selection Algorithm To Diagnose Haematological Diseases

Yıl 2014, Cilt: 5 Sayı: 16, 7 - 20, 01.07.2014
https://doi.org/10.5824/1309-1581.2014.3.001.x

Öz

The aim of this study is to develop a method to improve the classification performance by haematological parameters. In classification problems it has been seen that kNN classifier is often used with the clonal selection algorithm. In this study unlike other studies Gini algorithm is performed instead of kNN classification algorithm and higher success rate is obtained. According to the World Health Organisation’s data nearly 10% of women in the world are anaemia. Anaemia is a disease that disrupts life quality and results in serious effects if not cured. Iron deficiency anaemia is the most common type of anaemia and women suffers this disease comparatively to men. Therefore, in this study, anaemia was preferred as a sample application. It is expected to reach successful results in diagnosis of other diseases by looking at haematological parameters with the proposed method. At the end of the study success ratios of different methods are compared by Receiver Operating Characteristics analysis method. While accuracy in memory-based classification is found as 96%, accuracy in regression tree method classification is 98.73%. Using Gini algorithm instead of kNN a higher success ratio is achieved so CSA surpassed ANN’s success ratio.

Kaynakça

  • Bozkurt, M. R., Yurtay, N., Yilmaz, Z., & Sertkaya, C. (2014). Comparison of different methods for determining diabetes. Turkish Journal of Electrical Engineering & Computer Sciences,22(4), 1044–1055. doi:10.3906/elk-1209-82
  • De Castro, L. N., & Timmis, J. (2002). Artificial Immune Systems: A Novel Paradigm to Pattern Recognition. InUniversity of Paisley (pp. 67–84). Springer Verlag, University of Paisley, UK.
  • De Castro, L. N., & Von Zuben, F. J. (2002). Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation,6(3), 239–251. doi:10.1109/TEVC.2002.1011539
  • De Castro, L. N., & Von Zuben, F. J. (2002). The Clonal Selection Algorithm with Engineering Applications. InIn GECCO 2002 - Workshop Proceedings (pp. 36–37). Morgan Kaufmann.
  • De Castro, L. N. & Von Zuben, F. J. (1999). Artificial Immune Systems: Part I-Basic Theory and Applications. Technical Report, TR-DCA 01/99.
  • Er, O., Yumusak, N., & Temurtas, F. (2012). Diagnosis of chest diseases using artificial immune system. Expert Systems with Applications, 39(2), 1862–1868. doi:10.1016/j.eswa.2011.08.064
  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters (vol 27, pp 861-874).
  • Garrett, S. M. (2005). How Do We Evaluate Artificial Immune Systems? Evol. Comput., 13(2), 145–177. doi:10.1162/1063656054088512
  • Hart, E., Ross, P., & Nelson, J. (1998). Producing robust schedules via an artificial immune system. IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (pp. 464–469). doi:10.1109/ICEC.1998.699852
  • Hillman, R. S., Ault, K. A., Leporrier, M. & Rinder, H. M. (2012). Hematology in Clinical Practice. McGraw-Hill Publisher.
  • Jun, J.-H., Lee, D.-W., & Sim, K.-B. (1999). Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system. IEEE International Conference on Systems, Man, and Cybernetics. IEEE SMC ’99 Conference Proceedings (Vol. 6, pp. 614–619). doi:10.1109/ICSMC.1999.816622
  • Kihel, B. K., & Benyettou, M. (2011). Parkinson’s Disease Recognition Using Artificial Immune System. Journal of Software Engineering and Applications, 04(07), 391–395. doi:10.4236/jsea.2011.47045
  • Kodaz, H., Özşen, S., Arslan, A., & Güneş, S. (2009). Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease. Expert Systems with Applications, 36(2, Part 2), 3086–3092. doi:10.1016/j.eswa.2008.01.026
  • Latifolu, F., Kodaz, H., Kara, S., & Güneş, S. (2007). Medical application of Artificial Immune Recognition System (AIRS): Diagnosis of atherosclerosis from carotid artery Doppler signals. Computers in Biology and Medicine,37(8), 1092–1099. doi:10.1016/j.compbiomed.2006.09.00
  • Masala, G. L., Golosio, B., Cutzu, R., & Pola, R. (2013). A two-layered classifier based on the radial basis function for the screening of thalassaemia. Computers in Biology and Medicine,43(11), 1724–1731. doi:10.1016/j.compbiomed.2013.08.020
  • MATLAB® Documentation Neural Network Toolbox Help. (2014). Feedforward neural network, Release 2014a, The MathWorks Inc.
  • MATLAB® Documentation Neural Network Toolbox Help. (2014). Probabilistic neural networks, Release 2014a, The MathWorks Inc.
  • MedCalc for Windows, version 12.5 MedCalc Software, Ostend, Belgium.
  • Mohammad, A. H., & Zitar, R. A. (2011). Application of genetic optimized artificial immune system and neural networks in spam detection. Applied Soft Computing, 11(4), 3827–3845. doi:10.1016/j.asoc.2011.02.021
  • Mori, K., Tsukiyama, M., & Fukuda, T. (1997). Artificial immunity based management system for a semiconductor production line. IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation (Vol. 1, pp. 851–855 vol.1). doi:10.1109/ICSMC.1997.626207
  • Nasraoui, O., Dasgupta, D., & Gonzalez, F. (2002). The Promise and Challenges of Artificial Immune System Based Web Usage Mining. Workshop on Web Analytics at Second SIAM, International Conference on Data mining (SDM). Arlington.
  • Özaslan, E. & Delibaşı, T. 2012. Tusem Dahiliye. Ankara: Tusem Publisher.
  • Özkan, Y. (2013). Veri Madenciliği Yöntemleri (2nd ed.). Istanbul: Papatya Publisher.
  • Polat, K., Şahan, S., & Güneş, S. (2007). A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis. Expert Systems with Applications, 32(4), 1141–1147. doi:10.1016/j.eswa.2006.02.007
  • Sengur, A. (2008). An expert system based on principal component analysis, artificial immune system and fuzzy -NN for diagnosis of valvular heart diseases. Computers in Biology and Medicine, 38(3), 329–338. doi:10.1016/j.compbiomed.2007.11.004
  • Sobh, T. S., & Mostafa, W. M. (2011). A cooperative immunological approach for detecting network anomaly. Applied Soft Computing,11(1), 1275–1283. doi:10.1016/j.asoc.2010.03.004
  • Şahan, S., Polat, K., Kodaz, H., & Güneş, S. (2007). A new hybrid method based on fuzzy-artificial immune system and -nn algorithm for breast cancer diagnosis. Computers in Biology and Medicine, 37(3), 415–423. doi:10.1016/j.compbiomed.2006.05.003
  • Tjornfelt-Jensen, M., & Hansen, T. K. (1999). Robust solutions to job shop problems. In Proceedings of the 1999 Congress on Evolutionary Computation. CEC 99 (Vol. 2, p. -1144 Vol. 2). doi:10.1109/CEC.1999.782551
  • Turkish Statistical Institute. (2013). Health Survey 2012. Ankara: Printing Division.
  • Vos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., … Murray, C. J. (2012).
  • Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2163– 2196. doi:10.1016/S0140-6736(12)61729-2
  • Weinstein, S., Obuchowski, N. A. & Lieber, M. L.(2005). Fundamentals of Clinical Research for Radiologists. American Journal of Roentgenology (vol 184 , pp 14 -19).
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Research Article
Yazarlar

Burcu Çarklı Yavuz Bu kişi benim

Tuba Karagül Yıldız Bu kişi benim

Nilüfer Yurtay Bu kişi benim

Ziynet Pamuk Bu kişi benim

Yayımlanma Tarihi 1 Temmuz 2014
Gönderilme Tarihi 1 Temmuz 2014
Yayımlandığı Sayı Yıl 2014 Cilt: 5 Sayı: 16

Kaynak Göster

APA Çarklı Yavuz, B., Karagül Yıldız, T., Yurtay, N., Pamuk, Z. (2014). Comparison of K Nearest Neighbours And Regression Tree Classifiers Used With Clonal Selection Algorithm To Diagnose Haematological Diseases. AJIT-E: Academic Journal of Information Technology, 5(16), 7-20. https://doi.org/10.5824/1309-1581.2014.3.001.x