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Veteriner hekimliği alanında karar ağaçları uygulamalarının incelenmesi

Yıl 2023, Cilt: 94 Sayı: 2, 177 - 187, 15.06.2023
https://doi.org/10.33188/vetheder.1203378

Öz

Bilimsel araştırmalar sonucunda elde edilen verilerin analiz edilmesinde istatistiksel yöntemler önemli birer araçtır. Bununla birlikte; elde edilen verinin çok büyük olması gibi durumlarda klasik istatistiksel yöntemler yetersiz kalabilmektedir. Teknolojinin hızla gelişmesi ve bilgilerin depolanabilme kapasitelerinin artması, bilginin önemini daha da arttırmıştır. Bilginin önemli hale gelmesi, toplanan verinin büyük olması ve klasik istatistiksel yöntemlerin bu veriyi analiz etmede yetersiz kalması ise veri madenciliği gibi yöntemlerin doğmasına neden olmuştur. Veri madenciliği, dijital platformlarda depolanan devasa büyüklükteki veriler arasındaki örüntülerin değerlendirilmesi, çıkarımlar yapılması ve bunun sonucunda da anlamlı bilgiler elde edilmesi için uygulanan analizler olarak tanımlanmaktadır. Veteriner hekimliği; hayvan yetiştiriciliği, gıda güvenliği, gıda kalitesinin belirlenmesi, hayvan hastalıklarının yayılımı, hastalıkların teşhis ve tedavisi gibi birçok konuda veri üretilmesi nedeniyle veri madenciliğinin uygulanabileceği bir alandır. Bu derlemede veteriner hekimliği alanında son yıllarda yaygın bir şekilde kullanılmaya başlanan ve önemli bir sınıflandırma modeli olan karar ağaçları modelleme yönteminin içeriği ve kullanım alanlarının tanıtılması amaçlanmıştır

Kaynakça

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Examination of decision trees applications in the veterinary medicine

Yıl 2023, Cilt: 94 Sayı: 2, 177 - 187, 15.06.2023
https://doi.org/10.33188/vetheder.1203378

Öz

Statistical methods are important tools in the analysis of data obtained as a result of scientific research. However, in cases where the data obtained is very large, classical statistical methods may be insufficient. The rapid development of technology and the increase in the storage capacity of information have increased the importance of information even more. The fact that information has become important, the data collected is large, and classical statistical methods are insufficient to analyze this data has led to the emergence of methods such as data mining. Data mining is defined as the analysis applied to evaluate the patterns among the huge data stored on digital platforms and to make inferences to obtain meaningful information. Veterinary science is an area where data mining can be applied because it produces data on many subjects such as animal husbandry, food safety, determination of food quality, the spread of animal diseases, diagnosis and treatment of diseases. This review, it is aimed to introduce the content and usage areas of the decision tree modeling method, which has been widely used in the field of veterinary medicine in recent years and is an important classification model.

Kaynakça

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  • 47. Gagaoua M, Monteils V, Picard B. Decision tree, a learning tool for the prediction of beef tenderness using rearing factors and carcass characteristics. J Sci Food 2019; 99:1275-1283.
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  • 48. Ekiz B, Baygül O, Yalçıntan H, Özcan M. Comparison of the decision tree, artificial neural network and multiple regression methods for prediction of carcass tissues composition of goat kids. Meat Sci 2020; 161:108011.
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  • 49. Tamura T, Okubo Y, Deguchi Y, Koshikawa S, Takahashi M, Chida Y, Okada K. Dairy cattle behavior classifications based on decision tree learning using 3 axis neck mounted accelerometers. Anim Sci J 2019; 90:589-596.
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  • 51. Pascottini OB, Probo M, Leblanc SJ, Opsomer G, Hostens M. Assessment of associations between transition diseases and reproductive performance of dairy cows using survival analysis and decision tree algorithms. Prevent Vet Med 2020; 176:104908.
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  • 54. Sun Z, Samarasighe S, Jago J. Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks J Dairy Res 2010; 77:168-175.
  • 54. Sun Z, Samarasighe S, Jago J. Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks J Dairy Res 2010; 77:168-175.
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  • 55. Caraviello DZ, Weige KA, Craven M, Gianola D, Cook NB, Norlund KV, Fricke PM, Wiltbank MC. Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms. J Dairy Sci 2006; 89:4703-4722.
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  • 63. Zhao B, Xue B. Improving prediction accuracy using decision tree based meta strategy and multi-threshold sequential voting exemplified by miRNA target prediction. Genomics 2017; 109:227-232.
  • 63. Zhao B, Xue B. Improving prediction accuracy using decision tree based meta strategy and multi-threshold sequential voting exemplified by miRNA target prediction. Genomics 2017; 109:227-232.
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  • 66. Swain SN, Makunin A, Simanchal Dora A, Barık TK. SNP barcoding based on decision tree algorithm: A new tool for identification of mosquito species with special reference to Anopheles. Acta Tropica 2019; 199: 105152.
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Toplam 134 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Veteriner Cerrahi, Zootekni, Genetik ve Biyoistatistik
Bölüm ÇAĞRILI MAKALE / DERLEME
Yazarlar

Özgecan Korkmaz Ağaoğlu 0000-0002-7414-1725

Safa Gürcan 0000-0002-0738-1518

Erken Görünüm Tarihi 14 Haziran 2023
Yayımlanma Tarihi 15 Haziran 2023
Gönderilme Tarihi 12 Kasım 2022
Kabul Tarihi 9 Mart 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 94 Sayı: 2

Kaynak Göster

Vancouver Korkmaz Ağaoğlu Ö, Gürcan S. Veteriner hekimliği alanında karar ağaçları uygulamalarının incelenmesi. Vet Hekim Der Derg. 2023;94(2):177-8.

Veteriner Hekimler Derneği Dergisi açık erişimli bir dergi olup, derginin yayın modeli Budapeşte Erişim Girişimi (BOAI) bildirisine dayanmaktadır. Yayınlanan tüm içerik, çevrimiçi ve ücretsiz olarak sunulan Creative Commons CC BY-NC 4.0 lisansı altında lisanslanmıştır. Yazarlar, Veteriner Hekimler Derneği Dergisi'nde yayınlanan eserlerinin telif haklarını saklı tutarlar.


Veteriner Hekimler Derneği / Turkish Veterinary Medical Society