Using machine learning to improve bovine tuberculosis control in herd level outbreaks
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023, 2023, pp. 1-4
- Issue Date:
- 2023-12-11
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| Using_machine_learning_to_improve_bovine_tuberculosis_control_in_herd_level_outbreaks.pdf | Published version | 756.79 kB |
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Bovine tuberculosis bTB a chronic disease of cattle is caused by the Mycobacterium bovis infection Despite having a serious social and economic impact in the United Kingdom and Ireland there is no antemortem gold standard diagnostic test Tuberculin skin tests CICT are commonly used as a control measure with the interferon gamma IFN assay being applied in certain circumstances This paper utilizes data gathered describing tuberculin regression in reactors test positive cattle following the CICT at 72 4 h post injection in herds with large bTB outbreaks The work then applies machine learning techniques Decision Trees Bagging Trees and Random Forests alongside several balancing approaches to predict which cattle were likely to be truly infected with tuberculosis enabling identification of atypical breakdowns that require extra investigation and providing a mechanism for quality assurance of the existing CICT bTB surveillance scheme The analysis showed that Random Forests RF trained using SMOTE balancing had the joint best performance and accuracy 0 90 The importance of the two components of the interferon gamma assay within the RF model also indicated that varying the assay threshold for large outbreaks would be beneficial Furthermore the combined use of the RF and IFN models could lead to the improved detection of infection within breakdown herds reducing the scale and duration of outbreaks An additional use of these models would be for quality assuring the current bTB surveillance based on CICT and post mortem inspection Quality control is well recognized as an essential component of a disease surveillance eradication programme Clinical Relevance Bovine tuberculosis remains a disease that is hard to control on a national level The use of the machine learning model could lead to significant improved detection of infection within breakdown herds reducing the scale and duration of outbreaks Advanced modelling such as this has t
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