Predicting Subclinical Ketosis in Dairy Cows Using Machine Learning Techniques
Autor: | Edyta Bauer, Alicja Satoła |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
metabolic disorder
media_common.quotation_subject Veterinary medicine acetone Machine learning computer.software_genre Logistic regression Article 03 medical and health sciences β-hydroxybutyrate SF600-1100 Feature (machine learning) Dairy cattle 030304 developmental biology Mathematics media_common 0303 health sciences Variables General Veterinary business.industry logistic regression 0402 animal and dairy science Regression analysis 04 agricultural and veterinary sciences 040201 dairy & animal science Regression Support vector machine QL1-991 Animal Science and Zoology Anomaly detection Artificial intelligence hyperketonemia business computer Zoology |
Zdroj: | Animals, Vol 11, Iss 2131, p 2131 (2021) Animals Volume 11 Issue 7 Animals : an Open Access Journal from MDPI |
ISSN: | 2076-2615 |
Popis: | The diagnosis of subclinical ketosis in dairy cows based on blood ketone bodies is a challenging and costly procedure. Scientists are searching for tools based on results of milk performance assessment that would allow monitoring the risk of subclinical ketosis. The objective of the study was (1) to design a scoring system that would allow choosing the best machine learning models for the identification of cows-at-risk of subclinical ketosis, (2) to select the best performing models, and (3) to validate them using a testing dataset containing unseen data. The scoring system was developed using two machine learning modeling pipelines, one for regression and one for classification. As part of the system, different feature selections, outlier detection, data scaling and oversampling methods were used. Various linear and non-linear models were fit using training datasets and evaluated on holdout, testing the datasets. For the assessment of suitability of individual models for predicting subclinical ketosis, three β-hydroxybutyrate concentration in blood (bBHB) thresholds were defined: 1.0, 1.2 and 1.4 mmol/L. Considering the thresholds of 1.2 and 1.4, the logistic regression model was found to be the best fitted model, which included independent variables such as fat-to-protein ratio, acetone and β-hydroxybutyrate concentrations in milk, lactose percentage, lactation number and days in milk. In the cross-validation, this model showed an average sensitivity of 0.74 or 0.75 and specificity of 0.76 or 0.78, at the pre-defined bBHB threshold 1.2 or 1.4 mmol/L, respectively. The values of these metrics were also similar in the external validation on the testing dataset (0.72 or 0.74 for sensitivity and 0.80 or 0.81 for specificity). For the bBHB threshold at 1.0 mmol/L, the best classification model was the model based on the SVC (Support Vector Classification) machine learning method, for which the sensitivity in the cross-validation was 0.74 and the specificity was 0.73. These metrics had lower values for the testing dataset (0.57 and 0.72 respectively). Regression models were characterized by poor fitness to data (R2 < 0.4). The study results suggest that the prediction of subclinical ketosis based on data from test-day records using classification methods and machine learning algorithms can be a useful tool for monitoring the incidence of this metabolic disorder in dairy cattle herds. |
Databáze: | OpenAIRE |
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