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Updated: Jul 13, 2026

Milk Collection Methods for Mice and Reeves' Muntjac Deer

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Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat

Jamie Imada¹, Juan Carlos Arango-Sabogal², Cathy Bauman¹

¹Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada.

Animals : an Open Access Journal From MDPI

|April 13, 2024

Summary

Machine learning, specifically random forest models, can predict future Johne's disease (a bovine illness) test results using milk data. This offers a promising approach for targeted Johne's testing in dairy herds.

Keywords:

Johne’s disease cattle dairy farming decision tree diagnostics disease control machine learning paratuberculosis random forest

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Area of Science:

Veterinary Medicine
Animal Husbandry
Data Science
Machine Learning Applications

Background:

Machine learning (ML) applications in animal husbandry and veterinary medicine are emerging.
The use of ML for Johne's disease (paratuberculosis) diagnosis and control remains limited.
Johne's disease poses significant economic challenges to the dairy industry.

Purpose of the Study:

To explore the efficacy of tree-based ML algorithms (decision trees, random forest) in predicting Johne's disease.
To analyze repeat milk testing data for predictive modeling of future Johne's test outcomes.
To assess the performance of ML models in diagnosing Johne's disease in dairy cows.

Main Methods:

Applied decision tree and random forest algorithms to milk testing data from 1197 Canadian dairy cows.
Utilized milk component testing results and historical Johne's test data as input features.
Evaluated model performance using metrics such as kappa, ROC AUC, sensitivity, specificity, and predictive values.

Main Results:

Random forest models incorporating milk components and past Johne's results showed strong predictive performance for dichotomous Johne's ELISA outcomes.
The optimal random forest model achieved a kappa of 0.626, ROC AUC of 0.915, 72% sensitivity, and 98% specificity.
Decision tree models offered interpretability with a marginal reduction in sensitivity compared to random forest.

Conclusions:

Tree-based machine learning algorithms, particularly random forest, demonstrate significant potential for predicting Johne's disease status in dairy cattle.
These models can enhance the development of targeted Johne's testing strategies, improving disease management efficiency.
Further validation in real-world settings and integration into control programs are recommended.