Knowledge Discovery of Bovine Tuberculosis in the Eurasian Badger using Machine Learning Techniques
Bibliographic record
Abstract
Bovine tuberculosis (Mycobacterium bovis) is a disease of cattle with severe consequences for agriculture in the British Isles. The Eurasian badger (Meles meles) is implicated in the spread and maintenance of bovine tuberculosis in the cattle population and various measures have been trialed in badgers to control infection. A five-year pilot Test, Vaccinate and Remove investigation (TVR) was carried out in a 100km2area of Northern Ireland that tested, vaccinated, and removed infected badgers. This study used machine learning techniques in order to predict whether a badger has bovine tuberculosis using data collected from the TVR study. Several machine learning models – Decision Trees, Random Forests, Logistic Regression, XGBoost – were created and attempted in order to classify the data with the highest accuracy. Synthetic Minority Oversampling Technique (SMOTE) was also carried out due to imbalance in the data. The C5.0 decision tree model was chosen as the final model. This model was the most appropriate choice as it achieved a very high AUC score with a value of 0.974 in training and 0. 962 in testing. It also had the benefit of being a white-box model. Almost all of the variables were found to be significant, including the visual diagnostic tests used in the study, thus supporting their importance. The final model gives confidence in current diagnostic tests to accurately identify infected badgers and helps to inform future diagnostic test regimes. This study represents one of the first applications of machine learning in wildlife disease control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".