APPLICATION AREA OF CLASSIFICATION TECHNIQUES IN MEDICINE

Yıl 2018, Cilt: 2 Sayı: 2, 1 - 8, 01.02.2019

Oğuz Ata Mustafa Fayez

Öz

Abstract

The health care industry produces a huge amount of data that collects complex patient and medical information.

Data mining is popular in various fields of research because of its applications and methodologies

to extract information correctly. Data mining techniques have the capabilities to find out veiled forms or relationships

among the objects in the medical data. In addition the most data mining algorithms that had

used in medical industry until this time are neural network including deep learning, SVM, Bayesian and fizzy

logic. The main reason of use these algorithms that because they are gave best results with high accuracy

with different type of medicine datasets. Finally, data mining continues with medicine industry to help people

with or solve different clinical problems.

Anahtar Kelimeler

Data mining algorithms, Medicine, Classification

TIPTA SINIFLANDIRMA YÖNTEMLERİNİN UYGULAMA ALANLARI

Öz

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.

Anahtar Kelimeler

Veri madenciliği, Algoritmaları, Tıp, Sınıflandırma

Kaynakça

• 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.
• 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.
• 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.
• 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.
• D. Landuyt, A. Grêt-Regamey, and R. Haines-Young. 2017. “Bayesian belief networks,” in Mapping Ecosystem Services, ed: Pensoft Publishers, pp. 138-143.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• M. Koch. 2018. “Artificial intelligence is becoming natural,” Cell, vol. 173, pp. 531-533.
• O. Er, N. Yumusak, and F. Temurtas. 2010. “Chest diseases diagnosis using artificial neural networks,” Expert Systems with Applications, vol. 37, pp. 7648-7655.
• 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.
• S. Patel and H. Patel. 2016. “Survey of data mining techniques used in healthcare domain,” International Journal of Information, vol. 6.
• 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.
• 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.
• 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.
• 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.
• 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.