Use of Multivariate Adaptive Regression Splines, Classification Tree and Roc Curve in Diagnosis of Subclinical Mastitis in Dairy Cattle


Δημοσιευμένα: Apr 29, 2022
Yasin ALTAY
https://orcid.org/0000-0003-4049-8301
İbrahim AYTEKİN
https://orcid.org/0000-0001-7769-0685
Ecevit EYDURAN
https://orcid.org/0000-0001-7200-982X
Περίληψη

Subclinical mastitis is one of the most significant diseases that cause economic losses in dairy cattle farming. This investigation was conducted on 112 head Holstein Friesian cows in order to reveal relationship between subclinical mastitis and electrical conductivity milk composition and milk quality. In the study, CMT (California Mastitis Test) and CSCC (Classified Somatic Cell Count) used in diagnosis of subclinical mastitis were used as a binary response variable i.e. healthy and unhealthy. Potential predictors included here were lactation number, days in milk (DIM), L, a, b, H, C, milk fat, milk protein, lactose, milk freezing point, SNF, density, solids, pH and electrical conductivity. CART, CHAID, Exhaustive CHAID, QUEST and multivariate adaptive regression splines (MARS) were used as data mining algorithms that help to make an accurate decision about detecting influential factors increasing risk of subclinical mastitis.

In conclusion, better classification performances of CART and MARS data mining algorithms were determined compared with those of remaining algorithms in order to correctly discriminate healthy and unhealthy cows.

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Αναφορές
Akin M, Eyduran E, Reed BM (2017) Use of rsm and chaid data mining algorithm for predicting mineral nutrition of hazelnut, Plant Cell, Tissue and Organ Culture (PCTOC), 128(2), 303–316.
Akin M, Eyduran SP, Eyduran E, Reed BM (2020) Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. Plant Cell, Tissue and Organ Culture (PCTOC), 140(3), 661–670.
Akin M, Hand C, Eyduran E, Reed BM (2018) Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees, Plant Cell, Tissue and Organ Culture (PCTOC), 132(3), 545–559.
Altay Y, Kılıç B, Aytekin I, Keskin I (2019) Determination of factors affecting mastitis in Holstein Friesian and Brown Swiss by using logistic regression analysis, Selcuk Journal of Agriculture and Food Sciences, 33(3), 194-197.
Akdag F, Gürler H, Teke B, Ugurlu M, Koçak O. (2017) The effect of the difference in the evaluation of cmt scores and scores in jersey cows on milk yield, milk components and subclinical mastitis diagnosis, Journal of the Faculty of Veterinary Medicine, 43(1), 44-52.
Aytekin I, Eyduran E, Keskin I (2018) Detecting the relationship of california mastitis test (CMT) with electrical conductivity, composition and quality of the milk in Holstein-Friesian and Brown Swiss cattle breeds using cart analysis, Fresenius Environmental Bulletin, 27(6), 4559-4565.
Aytekin İ, Boztepe S (2014) Somatic cell count, importance and effect factors in dairy cattle, Turkish Journal of Agriculture – Food Science and Technology, 2, 112-121.
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984). Classification and regression trees. Chapman & Hall/CRC
Biggs D, De Ville B, Suen E (1991) A method of choosing multiway partitions for classifi cation and decision trees. Journal of Applied Statistics, 18(1), 49–62.
Coskun FS, Zulkadir U (2018) The use of fuzzy logic approach in evaluation of subclinic mastitis, Selcuk Journal of Agriculture and Food Sciences, 32(3), 436-439.
Da Costa Ribeiro AB, Sifuentes J, Zanol D, Lombarde LL (2016) Evaluation of an electrical conductivity portable device as an alternative for subclinical mastitis detection, Revista de Salud Animal, 38, 131-135.
De Oliveira Moura E, Do Nascimento Rangel AH, Borba LHF, Júnior JGBG, Da Costa Lima GF, De Lima Júnior DM, De Aguiar EM (2017) Electrical conductivity and somatic cell count in zebu cow’s milk, Semina: Ciências Agrárias, 38(5), 3231-3240.
El-Sayed SM, Awad IE, Shalapy SM (2015) A study on the bacteria causing subclinical mastitis in dairy cows and its effect on somatic cell count and milk chemical composition parameters, Zagazig Veterinary Journal, 43(1), 26-35.
Eyduran E, Ozdemir T, Yazgan K, Keskin S (2005) The effects of lactation rank and period on somatic cell count (scc) in milks of holstein cows, Van Veterinary Journal. 16, 61-65.
Eyduran E, Yılmaz I, Kaygısız A, Aktaş ZM (2013) An investigation on relationship between lactation milk yield, somatic cell count and udder traits in first lactation Turkish Saanen goat using different statistical techniques, The Journal of Animal & Plant Sciences. 23, 956- 963.
Eyduran E, Akin M, Eyduran SP (2019) Application of multivariate adaptive regression splines in agricultural sciences through R software. Ankara: Nobel Academic Publishing.
Friedman JH, Roosen CB (1995) An introduction to multivariate adaptive regression splines, Statistical Methods in Medical Research, 4(3), 197–217.
Friedman JH (1991) Multivariate adaptive regression splines, Annals of Statistics. 19, 1-141.
IBM Corp. Released, (2015). IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp.
Islam MA, Islam MZ, Rahman MS, Islam MT (2011) Prevalence of subclinical mastitis in dairy cows in selected areas of Bangladesh, Bangladesh Journal of Veterinary Medicine, 9(1), 73-78.
Gáspárdy A, Ismach G, Bajcsy Á, Veress G, Márkus S, Komlósi I (2012) Evaluation of the on-line electrical conductivity of milk in mastitic dairy cows, Acta Veterinaria Hungarica. 60, 145-55.
Grzesiak W, Zaborski D (2012) Examples of the use of data mining methods in animal breeding. In: Data mining applications in engineering and medicine (ed. A Karahoca). InTech, Rijeka, Croatia, pp. 303-324. https://doi.org/10.5772/50893
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29–36
Kass GV (1980) An exploratory technique for investigating large quantities of categorical data, Journal of the Royal Statistical Society: Series C (Applied Statistics), 29(2), 119-127.
Kaşıkçı G, Cetin O, Bingol EB, Gunduz MC (2012) Relations between electrical conductivity, somatic cell count, California mastitis test and some quality parameters in the diagnosis of subclinical mastitis in dairy cows, Turkish Journal of Veterinary and Animal Sciences. 36, 49-55.
Kılıç B, Keskin I (2019) Determination of factors effective in diagnosis of mastitis in holstein cattle by logistic regression analysis, Journal of Bahri Dagdas Animal Research, 8 (2), 46-55.
Kovalchuk IY, Mukhitdinova Z, Turdiyev T, Madiyeva G, Akin M, Eyduran E, Reed BM (2017) Modeling some mineral nutrient requirements for micropropagated wild apricot shoot cultures, Plant Cell, Tissue and Organ Culture (PCTOC), 129(2), 325–335.
Kovalchuk IY, Mukhitdinova Z, Turdiyev T, Madiyeva G, Akin M, Eyduran E, Reed BM (2018) Nitrogen ions and nitrogen ion proportions impact the growth of apricot (prunus armeniaca) shoot cultures, Plant Cell, Tissue and Organ Culture (PCTOC), 133(2), 263–273.
Kuhn M (2020) Caret: Classification and regression training. Retrieved from https://CRAN.R-project.org/package=caret
Kuhn M, Johnson K (2013) Applied predictive modeling. New York: Springer.
Loh WY, Shih YS (1997) Split selection methods for classification trees, Statistica Sinica, 7, 815-840.
Mammadova NM, Keskin I (2015) Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle, Indian Journal of Animal Research. 49, 671-679.
Mikail N, Keskin I (2015) Subclinical mastitis prediction in dairy cattle by application of fuzzy logic, Pakistan Journal of Agricultural Sciences. 52, 1101-1107.
Milborrow S (2019) Earth: Multivariate adaptive regression splines. Retrieved from https://CRAN.R-project.org/package=earth
Mpatswenumugabo JP, Bebora LC, Gitao GC, Mobegi VA, Iraguha B, Kamana O, Shumbusho B (2017) Prevalence of subclinical mastitis and distribution of pathogens in dairy farms of Rubavu and Nyabihu districts, Rwanda. Journal of Veterinary Medicine, 1-8, https://doi.org/10.1155/2017/8456713.
R Core Team (2020) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
Sharma N, Singh NK, Bhadwal MS (2011) Relationship of somatic cell count and mastitis: An overview, Asian-Australasian Journal of Animal Sciences, 24(3), 429-438.
Špakauskas V, Klimien I, Matuseviius A (2006) A comparison of indirect methods for diagnosis of subclinical mastitis in lactating dairy cows, Veterinarski arhiv. 76, 101-109.
Ural AD (2013) The relationships among some udder traits and somatic cell count in holstein-friesian cows, Kafkas Universitesi Veteriner Fakultesi Dergisi, 19(4), 601-613.
Tiwari S, Mohanty TK, Patbandha TK, Kumaresan A, Bhakat M, Kumar N, Baithalu RK (2017) Critical thresholds of milk scc, ec and ph for detection of sub-clinical mastitis in crossbred cows reared under subtropical agroclimatic condition, International Journal of Livestock Research, 8(6), 152-159.
Tyasi TL, Eyduran E, Celik S (2021) Comparison of tree-based regression tree methods for predicting live body weight from morphological traits in Hy-line silver brown commercial layer and indigenous Potchefstroom Koekoek breeds raised in South Africa, Trop Anim Health Prod, https://doi.org/10.1007/s11250-020-02443-y