Derleme
A Review on Determination of Computer Aid Diagnosis and/or Risk Factors Using Data Mining Methods in Veterinary Field
Öz
Data mining is an interdisciplinary field. In this field, statistical analysis techniques and artificial intelligence algorithms are used. Thanks to the algorithms used, hidden information within the data is revealed and transformed into qualified information. Data mining techniques have been used effectively in health, engineering, biomedicine and many other fields for many years, and are known to contribute significantly to health sciences. However, the use of this effective method in animal health is very limited and the use of data mining methods in animal health has accelerated in recent years. Animal illness and mortality cause decrease husbandry and this impact negatively on the national economy. Interdisciplinary studies are very important to increase profitability in this field. In this review, studies on the determination of animal diseases and/or risk factors using data mining methods have been examined. Studies in the veterinary field shows the data mining methods can be successfully applied in this field.
Anahtar Kelimeler
Kaynakça
- 1. Alpaydin E., 2014. Introduction to machine learning. 3rd ed., MA: The MIT Press, Cambridge. 2. Witten IH., Frank E., Hall MA., Pal CJ., 2014. Data Mining: Practical machine learning tools and techniques. 4th ed., Morgan Kaufmann Publishers, San Francisco. 3. Han J., Pei J., Kamber M., 2011. Data mining: concepts and techniques. 3rd ed., Morgan Kaufmann Publishers, Amsterdam. 4. Tomar D., Agarwal S., 2013. A survey on Data Mining approaches for Healthcare. J BioSci Biotechnol, 5, 241–266. 5. Cihan P., Gökçe E., Kalıpsız O., 2017. A review of machine learning applications in veterinary field. Kafkas Univ Vet Fak Derg, 23, 673-680. DOI:10.9775/kvfd.2016.17281 6. Küçükönder H., Üçkardeş F., Narinç D., 2014. A data mining application in animal breeding: Determination of some factors in Japanese Quail Eggs affecting fertility. Kafkas Univ Vet Fak Derg, 20, 903-908. 7. Uckardes F., Narinc D., Kucukonder H., Rathert TC., 2014. Application of classification tree method to determine factors affecting fertility in Japanese quail eggs. J Anim Sci Adv, 4, 1017-1023. 8. Mehraban Sangatash M., Mohebbi M., Shahidi F., Vahidian Kamyad A., Qhods Rohani M., 2012. Application of fuzzy logic to classify raw milk based on qualitative properties. Int J Agrisci, 2, 1168-1178. 9. Akıllı A., Atıl H., Kesenkaş H., 2014. Çiğ süt kalite değerlendirmesinde bulanık mantık yaklaşımı. Kafkas Univ Vet Fak Derg, 20, 223-229. 10. Memmedova N., Keskin İ., 2011. İneklerde bulanık mantık modeli ile hareketlilik ölçüsünden yararlanılarak kızgınlığın tespiti. Kafkas Univ Vet Fak Derg, 17, 1003-1008. 11. Zarchi HA., Jonsson R., Blanke M., 2009. Improving oestrus detection in dairy cows by combining statistical detection with fuzzy logic classification. In Proceedings of the 7th Workshop on Advanced Control and Diagnosis (ACD), Zielona, 20. 12. Brown-Brandl TM., Jones DD., Woldt WE., 2005. Evaluating modelling techniques for cattle heat stress prediction. Biosyst Eng, 91, 513-524. 13. Sharma AK., Sharma RK., Kasana HS., 2007. Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Appl Soft Comput, 7, 1112-1120. 14. Chen LJ., Cui LY., Xing L., Han LJ., 2008. Prediction of the nutrient content in dairy manure using artificial neural network modeling. J Dairy Sci, 91, 4822-4829. 15. Kuncheva LI., del Rio Vilas VJ., Rodriguez JJ., 2007. Diagnosing scrapie in sheep: A classification experiment. Comput Biol Med, 37, 1194-1202. 16. Çelik B., Akçapınar H., 2006. Ankara Keçisinin tiftik özellikleri yönünde kümeleme analizi. Lalahan Hay Arast Enst Derg, 46, 19-27. 17. Küçükönder H., Ayaşan T., Hizli H., 2015. Classification of holstein dairy cattles in terms of parameters some milk component belongs by using the fuzzy cluster analysis. Kafkas Univ Vet Fak Derg, 21, 601-606. 18. Gevrekçi Y., Ataç FE., Takma Ç., Akbaş Y., Taşkın T., 2011. Koyunculuk açısından Batı Anadolu illerinin sınıflandırılması. Kafkas Univ Vet Fak Derg, 17, 755-760. 19. Kılıç İ., Özbeyaz C., 2010. Bulanık kümeleme analizinin koyun yetiştiriciliğinde kullanımı ve bir uygulama. Kocatepe Vet J, 3, 31-37. 20. Niemi JK., Lyytikainen T., Sahlström L., Virtanen T., Lehtonen H., 2009. Risk classification in animal disease prevention: Who benefits from differentiated policy? Selected Paper Prepared for Presentation at the Agricultural and Applied Economics Association 2009 AAE and ACCI Join Annual Meeting, Milwaukee, Wisconsin. 21. Lyytikainen T., Kallio ER., 2008. Risk classification of finnish pig farms by simulated foot and mouth disease spread. Society for Veterinary Epidemiology and Preventive Medicine, Proceedings of a Meeting Held at Liverpook, 285-300. 22. Babcock AH., White BJ., Renter DG., Dubnicka SR., Scott HM., 2013. Predicting cumulative risk of bovine respiratory disease complex (BRDC) using feedlot arrival data and daily morbidity and mortality counts. Can J Vet Res, 77, 33-44. 23. Hermann-Bank ML., Skovgaard K., Stockmarr A., Strube ML., Larsen N., Kongsted H., Ingerslev HC., 2015. Characterization of the bacterial gut microbiota of piglets suffering from new neonatal porcine diarrhoea. BMC Vet Res, 11, 139. 24. Fayyad U., Piatetsky-Shapiro G., Smyth P., 1996. From data mining to knowledge discovery in databases. AI magazine, 17, 37. 25. Niaksu O., 2015. Development and application of data mining methods in medical diagnostics and healthcare management. Technological Sciences, Vilnius University, Vilnius. 26. Silver C., Lewins A., 2014. Using software in qualitative research: A step-by-step guide. 2nd ed., Sage publications, Los Angles. 27. Cios KJ., Pedrycz W., Swiniarski RW., 2007. Data mining: A Knowledge Discovery. Springer, New York. 28. Tan PN., Steinbach M., Kumar V., 2006. Introduction to Data Mining, Pearson Addison-Wesley, Boston. 29. Liu B., Hsu W., Ma Y., 1998. Integrating classification and association rule mining. The Fourth International Conference on Knowledge Discovery and Data Mining, New York City, 1-7. 30. Yazdanbakhsh O., Zhou Y., Dick S., 2017. An intelligent system for livestock disease surveillance. Information Sciences, 378, 26–47. 31. Saidani K., Lopez-Sandez C., Pablo DF., 2016. Effect of climate on the epidemiology of bovine hypodermosis in Algeria. Kafkas Univ Vet Fak Derg, 22, 147-154. 32. Awaysheh A., Wilcke J., Elvinger F., Rees L., Fan W., Zimmerman KL., 2016. Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats. J Vet Diagn Invest, 28, 679-687. 33. Boujenane I., El Aimani J., 2015. Incidence and occurrence time of clinical mastitis in Holstein cows. Turk J Vet Anim Sci, 39, 42-49. 34. Amrine DE., White BJ., Larson RL., 2014. Comparison of classification algorithms to predict outcomes of feedlot cattle identified and treated for bovine respiratory disease. Comput Electron Agric, 105, 9-19. 35. Urbach YK., Raber KA., Canneva F., Plank AC., Andreasson T., Ponten H., Kullingsjö J., 2014. Automated phenotyping and advanced data mining exemplified in rats transgenic for Huntington’s disease. J Neurosci Methods, 234, 38-53. 36. Ahmed H., Khan MR., Panadero-Fontan R., Sandez CL., Asif S., Mustafa I., Qayyum M., 2013. Influence of epidemiological factors on the prevalence and intensity of infestation by Hypoderma spp. (Diptera: Oestidae) in cattle of Potowar Region, Pakistan. Pakistan J Zool, 45, 1495-1500. 37. Piwczynski D., Sitkowska B., Wisniewska E., 2012. Application of classification trees and logistic regression to determine factors responsible for lamb mortality. Small Rumin Res, 103, 225-231. 38. Geenen PL., van der Gaag LC., Loeffen WLA., Elbers ARW., 2011. Constructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine fever. Res Vet Sci, 91, 64-70. 39. Lopez CM., Fernandez G., Vina M., Cienfuegos S., Panadero R., Vazquez L., Diez-Banos P., Morrondo P., 2011. Protostrongylid infection in meat sheep from Northwestern Spain: Prevalence and risk factors. Vet Parasitol, 178, 108-114. 40. Dawson, S., Else RW., Rhind SM., Collie DDS., 2005. Diagnostic value of cytology of bronchoalveolar fluid for lung diseases of sheep. Vet Rec, 157, 433-436. 41. Sandholm T., Brodley C., Vidovic A., Sandholm M., 1996. Comparison of regression methods, symbolic induction methods and neural networks in morbidity diagnosis and mortality prediction in equine gastrointestinal colic. AAAI 1996 Spring Symp Ser Artif Intell Med Appl Curr Technol, California, 154-159. 42. Ripley BD., 2001. The R project in statistical computing. MSOR Connections. The newsletter of the LTSN Maths, Stats & OR Network 2001, 1, 23-25. 43. Trewartha D., 2006. Investigating data mining in MATLAB. Rhodes University, Department of Science, South Africa. 44. Rangra K., Bansal KL., 2014. Comparative study of data mining tools. Int J Adv Res Comput Sci Softw Eng, 4, 216-223. 45. Pujari P., Gupta JB., 2012. Exploiting data mining techniques for improving the efficiency of time series data using spss-clementine. Res World J Arts Sci Commer, 3, 69. 46. Matignon R., 2007. Data mining using SAS enterprise miner. 1st ed., John Wiley & Sons, New York.
Veterinerlik Alanında Veri Madenciliği Yöntemleri Kullanılarak Bilgisayar Destekli Tanı ve/veya Risk Faktörlerinin Belirlenmesi Üzerine Bir İnceleme
Öz
Veri madenciliği disiplinler arası bir alandır. Bu alanda istatistiksel analiz teknikleri ve yapay zeka algoritmaları kullanılmaktadır. Kullanılan algoritmalar sayesinde verideki gizli bilgiler açığa çıkarılarak ve nitelikli bilgiye dönüştürülmektedir. Veri madenciliği teknikleri sağlık, mühendislik, biyomedikal ve diğer birçok alanda uzun yıllardır etkin bir şekilde kullanılmaktadır ve sağlık bilimlerine önemli ölçüde katkıda bulunduğu bilinmektedir. Ancak bu etkili yöntemin hayvan sağlığında kullanımı oldukça sınırlı olup son yıllarda hayvan sağlığında veri madenciliği yöntemlerinin kullanımı hızlanmıştır. Hayvan hastalıkları ve ölüm oranları hayvancılığın azalmasına neden olmakta ve bu da ülke ekonomisini olumsuz yönde etkilemektedir. Bu alandaki karlılığı artırmak için disiplinler arası çalışmalar çok önemlidir. Bu derlemede, veri madenciliği yöntemlerini kullanarak hayvan hastalıklarının ve / veya risk faktörlerinin belirlenmesine yönelik çalışmalar incelenmiştir. Veterinerlik alanındaki çalışmalar, veri madenciliği yöntemlerinin bu alanda başarıyla uygulanabileceğini göstermektedir.
Anahtar Kelimeler
Kaynakça
- 1. Alpaydin E., 2014. Introduction to machine learning. 3rd ed., MA: The MIT Press, Cambridge. 2. Witten IH., Frank E., Hall MA., Pal CJ., 2014. Data Mining: Practical machine learning tools and techniques. 4th ed., Morgan Kaufmann Publishers, San Francisco. 3. Han J., Pei J., Kamber M., 2011. Data mining: concepts and techniques. 3rd ed., Morgan Kaufmann Publishers, Amsterdam. 4. Tomar D., Agarwal S., 2013. A survey on Data Mining approaches for Healthcare. J BioSci Biotechnol, 5, 241–266. 5. Cihan P., Gökçe E., Kalıpsız O., 2017. A review of machine learning applications in veterinary field. Kafkas Univ Vet Fak Derg, 23, 673-680. DOI:10.9775/kvfd.2016.17281 6. Küçükönder H., Üçkardeş F., Narinç D., 2014. A data mining application in animal breeding: Determination of some factors in Japanese Quail Eggs affecting fertility. Kafkas Univ Vet Fak Derg, 20, 903-908. 7. Uckardes F., Narinc D., Kucukonder H., Rathert TC., 2014. Application of classification tree method to determine factors affecting fertility in Japanese quail eggs. J Anim Sci Adv, 4, 1017-1023. 8. Mehraban Sangatash M., Mohebbi M., Shahidi F., Vahidian Kamyad A., Qhods Rohani M., 2012. Application of fuzzy logic to classify raw milk based on qualitative properties. Int J Agrisci, 2, 1168-1178. 9. Akıllı A., Atıl H., Kesenkaş H., 2014. Çiğ süt kalite değerlendirmesinde bulanık mantık yaklaşımı. Kafkas Univ Vet Fak Derg, 20, 223-229. 10. Memmedova N., Keskin İ., 2011. İneklerde bulanık mantık modeli ile hareketlilik ölçüsünden yararlanılarak kızgınlığın tespiti. Kafkas Univ Vet Fak Derg, 17, 1003-1008. 11. Zarchi HA., Jonsson R., Blanke M., 2009. Improving oestrus detection in dairy cows by combining statistical detection with fuzzy logic classification. In Proceedings of the 7th Workshop on Advanced Control and Diagnosis (ACD), Zielona, 20. 12. Brown-Brandl TM., Jones DD., Woldt WE., 2005. Evaluating modelling techniques for cattle heat stress prediction. Biosyst Eng, 91, 513-524. 13. Sharma AK., Sharma RK., Kasana HS., 2007. Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Appl Soft Comput, 7, 1112-1120. 14. Chen LJ., Cui LY., Xing L., Han LJ., 2008. Prediction of the nutrient content in dairy manure using artificial neural network modeling. J Dairy Sci, 91, 4822-4829. 15. Kuncheva LI., del Rio Vilas VJ., Rodriguez JJ., 2007. Diagnosing scrapie in sheep: A classification experiment. Comput Biol Med, 37, 1194-1202. 16. Çelik B., Akçapınar H., 2006. Ankara Keçisinin tiftik özellikleri yönünde kümeleme analizi. Lalahan Hay Arast Enst Derg, 46, 19-27. 17. Küçükönder H., Ayaşan T., Hizli H., 2015. Classification of holstein dairy cattles in terms of parameters some milk component belongs by using the fuzzy cluster analysis. Kafkas Univ Vet Fak Derg, 21, 601-606. 18. Gevrekçi Y., Ataç FE., Takma Ç., Akbaş Y., Taşkın T., 2011. Koyunculuk açısından Batı Anadolu illerinin sınıflandırılması. Kafkas Univ Vet Fak Derg, 17, 755-760. 19. Kılıç İ., Özbeyaz C., 2010. Bulanık kümeleme analizinin koyun yetiştiriciliğinde kullanımı ve bir uygulama. Kocatepe Vet J, 3, 31-37. 20. Niemi JK., Lyytikainen T., Sahlström L., Virtanen T., Lehtonen H., 2009. Risk classification in animal disease prevention: Who benefits from differentiated policy? Selected Paper Prepared for Presentation at the Agricultural and Applied Economics Association 2009 AAE and ACCI Join Annual Meeting, Milwaukee, Wisconsin. 21. Lyytikainen T., Kallio ER., 2008. Risk classification of finnish pig farms by simulated foot and mouth disease spread. Society for Veterinary Epidemiology and Preventive Medicine, Proceedings of a Meeting Held at Liverpook, 285-300. 22. Babcock AH., White BJ., Renter DG., Dubnicka SR., Scott HM., 2013. Predicting cumulative risk of bovine respiratory disease complex (BRDC) using feedlot arrival data and daily morbidity and mortality counts. Can J Vet Res, 77, 33-44. 23. Hermann-Bank ML., Skovgaard K., Stockmarr A., Strube ML., Larsen N., Kongsted H., Ingerslev HC., 2015. Characterization of the bacterial gut microbiota of piglets suffering from new neonatal porcine diarrhoea. BMC Vet Res, 11, 139. 24. Fayyad U., Piatetsky-Shapiro G., Smyth P., 1996. From data mining to knowledge discovery in databases. AI magazine, 17, 37. 25. Niaksu O., 2015. Development and application of data mining methods in medical diagnostics and healthcare management. Technological Sciences, Vilnius University, Vilnius. 26. Silver C., Lewins A., 2014. Using software in qualitative research: A step-by-step guide. 2nd ed., Sage publications, Los Angles. 27. Cios KJ., Pedrycz W., Swiniarski RW., 2007. Data mining: A Knowledge Discovery. Springer, New York. 28. Tan PN., Steinbach M., Kumar V., 2006. Introduction to Data Mining, Pearson Addison-Wesley, Boston. 29. Liu B., Hsu W., Ma Y., 1998. Integrating classification and association rule mining. The Fourth International Conference on Knowledge Discovery and Data Mining, New York City, 1-7. 30. Yazdanbakhsh O., Zhou Y., Dick S., 2017. An intelligent system for livestock disease surveillance. Information Sciences, 378, 26–47. 31. Saidani K., Lopez-Sandez C., Pablo DF., 2016. Effect of climate on the epidemiology of bovine hypodermosis in Algeria. Kafkas Univ Vet Fak Derg, 22, 147-154. 32. Awaysheh A., Wilcke J., Elvinger F., Rees L., Fan W., Zimmerman KL., 2016. Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats. J Vet Diagn Invest, 28, 679-687. 33. Boujenane I., El Aimani J., 2015. Incidence and occurrence time of clinical mastitis in Holstein cows. Turk J Vet Anim Sci, 39, 42-49. 34. Amrine DE., White BJ., Larson RL., 2014. Comparison of classification algorithms to predict outcomes of feedlot cattle identified and treated for bovine respiratory disease. Comput Electron Agric, 105, 9-19. 35. Urbach YK., Raber KA., Canneva F., Plank AC., Andreasson T., Ponten H., Kullingsjö J., 2014. Automated phenotyping and advanced data mining exemplified in rats transgenic for Huntington’s disease. J Neurosci Methods, 234, 38-53. 36. Ahmed H., Khan MR., Panadero-Fontan R., Sandez CL., Asif S., Mustafa I., Qayyum M., 2013. Influence of epidemiological factors on the prevalence and intensity of infestation by Hypoderma spp. (Diptera: Oestidae) in cattle of Potowar Region, Pakistan. Pakistan J Zool, 45, 1495-1500. 37. Piwczynski D., Sitkowska B., Wisniewska E., 2012. Application of classification trees and logistic regression to determine factors responsible for lamb mortality. Small Rumin Res, 103, 225-231. 38. Geenen PL., van der Gaag LC., Loeffen WLA., Elbers ARW., 2011. Constructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine fever. Res Vet Sci, 91, 64-70. 39. Lopez CM., Fernandez G., Vina M., Cienfuegos S., Panadero R., Vazquez L., Diez-Banos P., Morrondo P., 2011. Protostrongylid infection in meat sheep from Northwestern Spain: Prevalence and risk factors. Vet Parasitol, 178, 108-114. 40. Dawson, S., Else RW., Rhind SM., Collie DDS., 2005. Diagnostic value of cytology of bronchoalveolar fluid for lung diseases of sheep. Vet Rec, 157, 433-436. 41. Sandholm T., Brodley C., Vidovic A., Sandholm M., 1996. Comparison of regression methods, symbolic induction methods and neural networks in morbidity diagnosis and mortality prediction in equine gastrointestinal colic. AAAI 1996 Spring Symp Ser Artif Intell Med Appl Curr Technol, California, 154-159. 42. Ripley BD., 2001. The R project in statistical computing. MSOR Connections. The newsletter of the LTSN Maths, Stats & OR Network 2001, 1, 23-25. 43. Trewartha D., 2006. Investigating data mining in MATLAB. Rhodes University, Department of Science, South Africa. 44. Rangra K., Bansal KL., 2014. Comparative study of data mining tools. Int J Adv Res Comput Sci Softw Eng, 4, 216-223. 45. Pujari P., Gupta JB., 2012. Exploiting data mining techniques for improving the efficiency of time series data using spss-clementine. Res World J Arts Sci Commer, 3, 69. 46. Matignon R., 2007. Data mining using SAS enterprise miner. 1st ed., John Wiley & Sons, New York.
Toplam 1 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Sağlık Kurumları Yönetimi |
| Bölüm | Derlemeler |
| Yazarlar | |
| Yayımlanma Tarihi | 25 Ekim 2019 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 14 Sayı: 2 |