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dc.contributor.authorUllomi, Joseph Oscar
dc.date.accessioned2024-08-16T09:00:21Z
dc.date.available2024-08-16T09:00:21Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/10832/3987
dc.description.abstractThe study aimed at developing a robust Machine Learning algorithm that could accurately predict Subclinical Mastitis in dairy cows, which often goes undetected until it is too late. We utilized five Machine Learning models, including Decision Trees, K-nearest neighbours, Logistic Regression, Random Forests, and eXtreme Gradient Boosting, to find the best fit for the work. Data was collected for two years from two large Hungarian dairy farms using two databases: RISKA and ALPRO. Somatic Cell Count values and Electroconductivity of Milk variables were the key features used for Artificial Neural Network-based classification. By utilizing the features of the Caret Package in the R environment, we filtered out correlated explanatory variables.en_US
dc.language.isoenen_US
dc.titleSubclinical Mastitis Detection by Machine Learningen_US
dc.typeThesisen_US


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