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

Abstract

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.

Keywords

• Bilgisayar Destekli Tanı
• K En Yakın Komşu
• Klonal Seçim Algoritması
• Regresyon Ağaçları
• Yapay Sinir Ağları

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

Abstract

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.

Keywords

• Artificial Neural Network
• Clonal Selection Algorithm
• Computer Aided Diagnosis
• K-Nearest Neighbours
• Regression Trees.

References

1. 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
2. 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.
3. 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
4. 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.
5. De Castro, L. N. & Von Zuben, F. J. (1999). Artificial Immune Systems: Part I-Basic Theory and Applications. Technical Report, TR-DCA 01/99.
6. 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
7. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters (vol 27, pp 861-874).
8. Garrett, S. M. (2005). How Do We Evaluate Artificial Immune Systems? Evol. Comput., 13(2), 145–177. doi:10.1162/1063656054088512

9. 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
10. Hillman, R. S., Ault, K. A., Leporrier, M. & Rinder, H. M. (2012). Hematology in Clinical Practice. McGraw-Hill Publisher.
11. 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
12. 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
13. 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
14. 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
15. 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
16. MATLAB® Documentation Neural Network Toolbox Help. (2014). Feedforward neural network, Release 2014a, The MathWorks Inc.
17. MATLAB® Documentation Neural Network Toolbox Help. (2014). Probabilistic neural networks, Release 2014a, The MathWorks Inc.
18. MedCalc for Windows, version 12.5 MedCalc Software, Ostend, Belgium.
19. 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
20. 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
21. 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.
22. Özaslan, E. & Delibaşı, T. 2012. Tusem Dahiliye. Ankara: Tusem Publisher.
23. Özkan, Y. (2013). Veri Madenciliği Yöntemleri (2nd ed.). Istanbul: Papatya Publisher.
24. 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
25. 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
26. 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
27. 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
28. 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
29. Turkish Statistical Institute. (2013). Health Survey 2012. Ankara: Printing Division.
30. Vos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., … Murray, C. J. (2012).
31. 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
32. Weinstein, S., Obuchowski, N. A. & Lieber, M. L.(2005). Fundamentals of Clinical Research for Radiologists. American Journal of Roentgenology (vol 184 , pp 14 -19).