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Journals / AURUM Mühendislik Sistemleri ve Mimarlık Dergisi / 2018 / Cilt: 2 Sayı: 2

APPLICATION AREA OF CLASSIFICATION TECHNIQUES IN MEDICINE

TIPTA SINIFLANDIRMA YÖNTEMLERİNİN UYGULAMA ALANLARI

Oğuz Ata(ALTINBAŞ ÜNİVERSİTESİ)0000-0003-4511-7694
Mustafa Fayez(ALTINBAŞ ÜNİVERSİTESİ)0000-0002-8123-8963
Pages
1–8
Publication Date
2019-02-01
DOI
—
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Abstract

Abstract

Özet

Özet Sağlık endüstrisi çok büyük miktarda veri üretmekte ve bu veriler karmaşık hasta ve sağlık bilgileri içermektedir. Veri madenciliği, veriden bilgi çıkarma uygulaması olduğundan pek çok alanda çok popülerdir. Veri madenciliği yöntemleri gerekli bilgi ve nesneleri medikal-tıbbi veriden çıkarılmak için de kullanılmaktadır. Bugüne dek tıp alanında kullanılan veri madenciliği algoritmaları derin öğrenme, SVM, Bayes ve bulanık mantıktır. Bunların kullanılmasındaki ana neden, farklı türdeki tıp verilerine çok doğru sonuçlar verebilme yetenekleridir. Veri madenciliği tıp alanında insanlara yardım etmeye ve çeşitli klinik sorunları çözmeye devam edecektir.

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Cite This Article

Ata, O., Fayez, M. (2019). APPLICATION AREA OF CLASSIFICATION TECHNIQUES IN MEDICINE. AURUM Mühendislik Sistemleri ve Mimarlık Dergisi, Cilt: 2 Sayı: 2, 1–8. https://ajindex.com/articles/1456470

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References (20)

  1. A. Fernández Hilario, A. Altalhi, S. Alshomrani, and F. Herrera. 2017. “Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?, International Journal of Computational Intelligence Systems, vol. 10(1), p. 1211-1225.
  2. A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, et al. 2017. “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, p. 115, 2017.
  3. C.-L. Chang and C.-H. Chen. 2009. “Applying decision tree and neural network to increase quality of dermatologic diagnosis,” Expert Systems with Applications, vol. 36, pp. 4035-4041.
  4. C. S. Dangare and S. S. Apte. 2012. “Improved study of heart disease prediction system using data mining classification techniques,” International Journal of Computer Applications, vol. 47, pp. 44-48.
  5. D. Landuyt, A. Grêt-Regamey, and R. Haines-Young. 2017. “Bayesian belief networks,” in Mapping Ecosystem Services, ed: Pensoft Publishers, pp. 138-143.
  6. D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, et al. 2018. “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell, vol. 172, pp. 1122-1131. e9.
  7. D. Shalvi and N. DeClaris. 1998. “An unsupervised neural network approach to medical data mining techniques,” in Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on, pp. 171-176.
  8. G. A. Longo, C. Zilio, L. Ortombina, and M. Zigliotto. 2017. “Application of Artificial Neural Network (ANN) for modeling oxide-based nanofluids dynamic viscosity,” International Communications in Heat and Mass Transfer, vol. 83, pp. 8-14.
  9. H. Ahuja and U. Batra. 2018. “Performance Enhancement in Requirement Prioritization by Using Least- Squares-Based Random Genetic Algorithm,” in Innovations in Computational Intelligence, ed: Springer, pp. 251-263.
  10. M. L. Raymer, W. F. Punch, E. D. Goodman, L. A. Kuhn, and A. K. Jain. 2000. “Dimensionality reduction using genetic algorithms,” IEEE transactions on evolutionary computation, vol. 4, pp. 164-171.
  11. M. Durairaj and V. Ranjani. 2013. “Data mining applications in healthcare sector: a study,” International journal of scientific & technology research, vol. 2, pp. 29-35.
  12. M. Koch. 2018. “Artificial intelligence is becoming natural,” Cell, vol. 173, pp. 531-533.
  13. O. Er, N. Yumusak, and F. Temurtas. 2010. “Chest diseases diagnosis using artificial neural networks,” Expert Systems with Applications, vol. 37, pp. 7648-7655.
  14. R. Das, I. Turkoglu, and A. Sengur. 2009. “Effective diagnosis of heart disease through neural networks ensembles,” Expert systems with applications, vol. 36, pp. 7675-7680.
  15. S. Patel and H. Patel. 2016. “Survey of data mining techniques used in healthcare domain,” International Journal of Information, vol. 6.
  16. S. ZahidHassan and B. Verma. 2007. “A hybrid data mining approach for knowledge extraction and classification in medical databases,” in Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on, pp. 503-510.
  17. V. Venkaiah. 2017. “Study Of Data Mining Techniques Used In Medicinal Services Domain,” International Journal For Research In Advanced Computer Science And Engineering (ISSN: 2208-2107), vol. 3, pp. 12-21.
  18. W.-L. Zuo, Z.-Y. Wang, T. Liu, and H.-L. Chen. 2013. “Effective detection of Parkinson’s disease using an adaptive fuzzy k-nearest neighbor approach,” Biomedical Signal Processing and Control, vol. 8, pp. 364-373.
  19. Y. J. Fei Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang. 2017. “Artificial intelligence in healthcare: past, present and future “ Stroke Vasc Neurol., 2.
  20. Y. Xing, J. Wang, and Z. Zhao. 2007. “Combination data mining methods with new medical data to predicting outcome of coronary heart disease,” in Convergence Information Technology, 2007. International Conference on, pp. 868-872.