Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations

dc.citation.issue6
dc.citation.volume11
dc.contributor.authorEbrahimie E
dc.contributor.authorMohammadi-Dehcheshmeh M
dc.contributor.authorLaven R
dc.contributor.authorPetrovski KR
dc.contributor.editorAlfson KJ
dc.contributor.editorClemmons EA
dc.contributor.editorDutton III JW
dc.coverage.spatialSwitzerland
dc.date.accessioned2024-02-01T19:36:46Z
dc.date.accessioned2024-07-25T06:41:45Z
dc.date.available2021-06
dc.date.available2024-02-01T19:36:46Z
dc.date.available2024-07-25T06:41:45Z
dc.date.issued2021-06-01
dc.description.abstractSubclinical mastitis, an economically challenging disease of dairy cattle, is associated with an increased use of antimicrobials which reduces milk quantity and quality. It is more common than clinical mastitis and far more difficult to detect. Recently, much attention has been paid to the development of machine-learning expert systems for early detection of subclinical mastitis from milking features. However, differences between animals within a farm as well as between farms, particularly across multiple years, are major obstacles to the generalisation of machine learning models. Here, for the first time, we integrated scaling by quartiling with classification based on associations in a multi-year study to deal with farm heterogeneity by discovery of multiple patterns towards mastitis. The data were obtained from one farm comprising Holstein Friesian cows in Ongaonga, New Zealand, using an electronic automated monitoring system. The data collection was repeated annually over 3 consecutive years. Some discovered rules, such as when the milking peak flow is low, electrical conductivity (EC) of milk is low, milk lactose is low, milk fat is high, and milk volume is low, the cow has subclinical mastitis, reached high confidence (>70%) in multiple years. On averages, over 3 years, low level of milk lactose and high value of milk EC were part of 93% and 83.8% of all subclinical mastitis detecting rules, offering a reproducible pattern of subclinical mastitis detection. The scaled year-independent combinational rules provide an easy-to-apply and cost-effective machine-learning expert system for early detection of hidden mastitis using milking parameters.
dc.description.confidentialfalse
dc.edition.editionJune 2021
dc.format.pagination1638-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/34205858
dc.identifier.citationEbrahimie E, Mohammadi-Dehcheshmeh M, Laven R, Petrovski KR. (2021). Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations.. Animals (Basel). 11. 6. (pp. 1638-).
dc.identifier.doi10.3390/ani11061638
dc.identifier.eissn2076-2615
dc.identifier.elements-typejournal-article
dc.identifier.issn2076-2615
dc.identifier.numberARTN 1638
dc.identifier.piiani11061638
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70700
dc.languageeng
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2076-2615/11/6/1638
dc.relation.isPartOfAnimals (Basel)
dc.rights(c) The author/sen
dc.rights.licenseCC BY 4.0en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectfarm heterogeneity
dc.subjectfarm management
dc.subjectinvisible mastitis
dc.subjectmachine learning
dc.subjectmeta-analysis
dc.subjectmilking parameters
dc.subjectsubclinical mastitis
dc.titleRule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations
dc.typeJournal article
pubs.elements-id446236
pubs.organisational-groupOther
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