ArticleScientific reports2021
Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications.The veterinary quarterly · 2026Review
- Infrared Thermography and Machine Learning for Mastitis Detection in Dairy Cows: A Pilot Case Study in Egyptian Farms.Veterinary sciences · 2026Article
- Integrating biomarkers and artificial intelligence for precision diagnostics in cattle health and herd management: a review.Veterinary research communications · 2026Review
- Machine learning-based prediction of subclinical mastitis in large-scale dairy herds using a locally established somatic cell count threshold.Scientific reports · 2026Article
- AI-Driven Multimodal Sensing for Early Detection of Health Disorders in Dairy Cows.Animals : an open access journal from MDPI · 2026Article
- Risk assessment of subclinical mastitis in Holstein cows in Jiangsu Province of China.Frontiers in veterinary science · 2026Article
- Milk Biomarkers and Herd Welfare Status in Dairy Cattle: A Machine Learning Approach.Veterinary sciences · 2025Article
- Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators.Animals : an open access journal from MDPI · 2025Article
- Neural Network-Aided Milk Somatic Cell Count Increase Prediction.Veterinary sciences · 2025Article
- Machine Learning Approach for Early Lactation Mastitis Diagnosis Using Total and Differential Somatic Cell Counts.Animals : an open access journal from MDPI · 2025Article
- Article
- Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count.Frontiers in veterinary science · 2025Article
- Machine Learning Techniques Associated With Infrared Thermography to Optimize the Diagnosis of Bovine Subclinical Mastitis.Veterinary medicine international · 2025Article
- Revealing microbial diversity in buffalo milk with high somatic cell counts: implications for mastitis diagnosis and treatment.Veterinary research communications · 2024Article
- Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests.Animals : an open access journal from MDPI · 2024Article
- Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud.Animals : an open access journal from MDPI · 2023Article
- A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems.Animals : an open access journal from MDPI · 2023Review
- Genomic and Phenotypic Udder Evaluation for Dairy Cattle Selection: A Review.Animals : an open access journal from MDPI · 2023Review
- CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring.Frontiers in artificial intelligence · 2023Article
- A Study on the Uniform Distribution and Counting Method of Raw Cow's Milk Somatic Cells.Micromachines · 2022Article
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Authors and funding
5 authors.
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Abstract
Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow's milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day.
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