Neural Network-Aided Milk Somatic Cell Count Increase Prediction
Subclinical mastitis (SM) is the most economically damaging yet often visually undetectable disease of dairy cows. Early detection and treatment can reduce the loss caused by the disease; thus, the continuous improvement of SM diagnostic methods is necessary. Although milk’s somatic cell count (SCC)...
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MDPI AG
2025-04-01
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| Series: | Veterinary Sciences |
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| Online Access: | https://www.mdpi.com/2306-7381/12/5/420 |
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| author | Sára Ágnes Nagy István Csabai Tamás Varga Bettina Póth-Szebenyi György Gábor Norbert Solymosi |
| author_facet | Sára Ágnes Nagy István Csabai Tamás Varga Bettina Póth-Szebenyi György Gábor Norbert Solymosi |
| author_sort | Sára Ágnes Nagy |
| collection | DOAJ |
| description | Subclinical mastitis (SM) is the most economically damaging yet often visually undetectable disease of dairy cows. Early detection and treatment can reduce the loss caused by the disease; thus, the continuous improvement of SM diagnostic methods is necessary. Although milk’s somatic cell count (SCC) is commonly measured for diagnostic purposes, its direct determination is not widely used in everyday practice. The primary objective of our work was to investigate whether the predictive value of SM diagnostics can be improved by training artificial neural networks (ANNs) on data generated using typical conventional milking systems. The best ANN classifier had a sensitivity of 0.54 and a specificity of 0.77, which is comparable to performances of various California Mastitis Tests (CMT) found in the literature. Combining two diagnostic tests, ANN and CMT, we concluded that the positive predictive value could be up to 50% higher than the value provided by the individual CMT. While implementing CMT is a labor-intensive process on herd-level, in milking machines where milk properties or milk yield data can be measured automatically, similar to our work, SCC-increase predictions for all individuals could be obtained daily basis. |
| format | Article |
| id | doaj-art-5f97a3a2a9f44fa98ab261c0324f922b |
| institution | OA Journals |
| issn | 2306-7381 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Veterinary Sciences |
| spelling | doaj-art-5f97a3a2a9f44fa98ab261c0324f922b2025-08-20T02:33:58ZengMDPI AGVeterinary Sciences2306-73812025-04-0112542010.3390/vetsci12050420Neural Network-Aided Milk Somatic Cell Count Increase PredictionSára Ágnes Nagy0István Csabai1Tamás Varga2Bettina Póth-Szebenyi3György Gábor4Norbert Solymosi5Department of Physics of Complex Systems, Eötvös Loránd University, 1117 Budapest, HungaryDepartment of Physics of Complex Systems, Eötvös Loránd University, 1117 Budapest, HungaryCentre for Bioinformatics, University of Veterinary Medicine, 1078 Budapest, HungaryDoctoral School of Animal Science, Hungarian University of Agriculture and Life Sciences, 7400 Kaposvár, HungaryCentre for Bioinformatics, University of Veterinary Medicine, 1078 Budapest, HungaryDepartment of Physics of Complex Systems, Eötvös Loránd University, 1117 Budapest, HungarySubclinical mastitis (SM) is the most economically damaging yet often visually undetectable disease of dairy cows. Early detection and treatment can reduce the loss caused by the disease; thus, the continuous improvement of SM diagnostic methods is necessary. Although milk’s somatic cell count (SCC) is commonly measured for diagnostic purposes, its direct determination is not widely used in everyday practice. The primary objective of our work was to investigate whether the predictive value of SM diagnostics can be improved by training artificial neural networks (ANNs) on data generated using typical conventional milking systems. The best ANN classifier had a sensitivity of 0.54 and a specificity of 0.77, which is comparable to performances of various California Mastitis Tests (CMT) found in the literature. Combining two diagnostic tests, ANN and CMT, we concluded that the positive predictive value could be up to 50% higher than the value provided by the individual CMT. While implementing CMT is a labor-intensive process on herd-level, in milking machines where milk properties or milk yield data can be measured automatically, similar to our work, SCC-increase predictions for all individuals could be obtained daily basis.https://www.mdpi.com/2306-7381/12/5/420dairy cowsubclinical mastitissomatic cell countelectrical conductivitymachine learningartificial neural network |
| spellingShingle | Sára Ágnes Nagy István Csabai Tamás Varga Bettina Póth-Szebenyi György Gábor Norbert Solymosi Neural Network-Aided Milk Somatic Cell Count Increase Prediction Veterinary Sciences dairy cow subclinical mastitis somatic cell count electrical conductivity machine learning artificial neural network |
| title | Neural Network-Aided Milk Somatic Cell Count Increase Prediction |
| title_full | Neural Network-Aided Milk Somatic Cell Count Increase Prediction |
| title_fullStr | Neural Network-Aided Milk Somatic Cell Count Increase Prediction |
| title_full_unstemmed | Neural Network-Aided Milk Somatic Cell Count Increase Prediction |
| title_short | Neural Network-Aided Milk Somatic Cell Count Increase Prediction |
| title_sort | neural network aided milk somatic cell count increase prediction |
| topic | dairy cow subclinical mastitis somatic cell count electrical conductivity machine learning artificial neural network |
| url | https://www.mdpi.com/2306-7381/12/5/420 |
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