{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T21:15:22Z","timestamp":1788556522173,"version":"build-2803163510"},"reference-count":51,"publisher":"American Dairy Science Association","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004569","name":"Ministry of Science and Higher Education of the Republic of Poland","doi-asserted-by":"publisher","award":["SUB\/020012-D015"],"award-info":[{"award-number":["SUB\/020012-D015"]}],"id":[{"id":"10.13039\/501100004569","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journalofdairyscience.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Dairy Science"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.3168\/jds.2026-28817","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T23:33:51Z","timestamp":1788478431000},"update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative analysis of artificial neural networks and classical machine learning models for predicting subclinical mastitis in dairy cows, with SHAP-based interpretation of predictions"],"prefix":"10.3168","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6609-4404","authenticated-orcid":false,"given":"A.","family":"Sato\u0142a","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7648-2359","authenticated-orcid":false,"given":"K.","family":"Sato\u0142a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1747","reference":[{"key":"10.3168\/jds.2026-28817_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.livsci.2021.104678","article-title":"Lactose and its relationship with other milk constituents, somatic cell count, and total bacterial count","volume":"252","author":"Alessio","year":"2021","journal-title":"Livest. Sci."},{"key":"10.3168\/jds.2026-28817_bib2","doi-asserted-by":"crossref","first-page":"8433","DOI":"10.3168\/jds.2020-18320","article-title":"Comparison of methods for predicting cow composite somatic cell counts","volume":"103","author":"Anglart","year":"2020","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib3","doi-asserted-by":"crossref","first-page":"11463","DOI":"10.3168\/jds.2023-24623","article-title":"Development and evaluation of predictive models for pregnancy risk in UK dairy cows","volume":"107","author":"Barden","year":"2024","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib4","first-page":"75","article-title":"Relationship of somatic cell count to daily yield and technological usefulness of milk from different breeds of cows","volume":"12","author":"Bar\u0142owska","year":"2009","journal-title":"Pol. J. Vet. Sci."},{"key":"10.3168\/jds.2026-28817_bib5","doi-asserted-by":"crossref","first-page":"332","DOI":"10.3390\/ani12030332","article-title":"The use of multilayer perceptron artificial neural networks to detect dairy cows at risk of ketosis","volume":"12","author":"Bauer","year":"2022","journal-title":"Animals (Basel)"},{"key":"10.3168\/jds.2026-28817_bib6","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1484","article-title":"Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges","volume":"13","author":"Bischl","year":"2023","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"10.3168\/jds.2026-28817_bib7","series-title":"Neural networks for pattern recognition","author":"Bishop","year":"1995"},{"key":"10.3168\/jds.2026-28817_bib8","doi-asserted-by":"crossref","first-page":"13642","DOI":"10.1038\/s41598-021-93056-4","article-title":"Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows","volume":"11","author":"Bobbo","year":"2021","journal-title":"Sci. Rep."},{"key":"10.3168\/jds.2026-28817_bib9","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.3168\/jds.2022-22292","article-title":"Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes","volume":"106","author":"Bobbo","year":"2023","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib10","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.livsci.2007.12.004","article-title":"Milk losses associated with somatic cell counts per breed, parity and stage of lactation in Canadian dairy cattle","volume":"117","author":"D\u00fcrr","year":"2008","journal-title":"Livest. Sci."},{"key":"10.3168\/jds.2026-28817_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103456","article-title":"Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep learning and gradient-boosted trees outperform other models","volume":"114","author":"Ebrahimi","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"10.3168\/jds.2026-28817_bib12","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.compag.2018.02.003","article-title":"Hierarchical pattern recognition in milking parameters predicts mastitis prevalence","volume":"147","author":"Ebrahimie","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.3168\/jds.2026-28817_bib13","doi-asserted-by":"crossref","first-page":"322","DOI":"10.3168\/jds.2014-8602","article-title":"Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks","volume":"98","author":"Ehret","year":"2015","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.idairyj.2021.105051","article-title":"Exploring machine learning algorithms for early prediction of clinical mastitis","volume":"119","author":"Fadul-Pacheco","year":"2021","journal-title":"Int. Dairy J."},{"key":"10.3168\/jds.2026-28817_bib15","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.3168\/jds.2020-19153","article-title":"Effect of subclinical mastitis detected in the first month of lactation on somatic cell count linear scores milk yield fertility and culling of dairy cows in certified organic herds","volume":"104","author":"Fernandes","year":"2021","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib16","doi-asserted-by":"crossref","first-page":"3929","DOI":"10.3168\/jds.2024-25404","article-title":"Usefulness of differential somatic cell count for udder health monitoring: Diagnostic performance of somatic cell count and differential somatic cell count for diagnosing intramammary infections in dairy herds with automated milking systems","volume":"108","author":"Fonseca","year":"2025","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib17","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","volume":"9","author":"Glorot","year":"2010","journal-title":"Proc. Mach. Learn. Res."},{"key":"10.3168\/jds.2026-28817_bib18","first-page":"1403","volume":"91","author":"Green","year":"2008","journal-title":"Cow, farm, and herd management factors in the dry period associated with raised somatic cell counts in early lactation"},{"key":"10.3168\/jds.2026-28817_bib19","doi-asserted-by":"crossref","DOI":"10.3389\/fvets.2025.1575525","article-title":"Research on the prediction model of mastitis in dairy cows based on time series characteristics","volume":"12","author":"Guo","year":"2025","journal-title":"Front. Vet. Sci."},{"key":"10.3168\/jds.2026-28817_bib20","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/01652176.2007.9695224","article-title":"Economic effects of bovine mastitis and mastitis management: A review","volume":"29","author":"Halasa","year":"2007","journal-title":"Vet. Q."},{"key":"10.3168\/jds.2026-28817_bib21","doi-asserted-by":"crossref","first-page":"599","DOI":"10.3168\/jds.2008-1564","article-title":"Production loss due to new subclinical mastitis in Dutch dairy cows estimated with a test-day model","volume":"92","author":"Halasa","year":"2009","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib22","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.3168\/jds.2017-12615","article-title":"Does clinical mastitis in the first 100 days of lactation 1 predict increased mastitis occurrence and shorter herd life in dairy cows?","volume":"101","author":"Hertl","year":"2018","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib23","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13028-017-0288-x","article-title":"Prevalence of subclinical mastitis in Finnish dairy cows: Changes during recent decades and impact of cow and herd factors","volume":"59","author":"Hiiti\u00f6","year":"2017","journal-title":"Acta Vet. Scand."},{"key":"10.3168\/jds.2026-28817_bib24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1080\/00480169.2011.547165","article-title":"Economic aspects of mastitis: new developments","volume":"59","author":"Hogeveen","year":"2011","journal-title":"N. Z. Vet. J."},{"key":"10.3168\/jds.2026-28817_bib25","author":"Ioffe"},{"key":"10.3168\/jds.2026-28817_bib26","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.compag.2009.08.012","article-title":"Decision-tree induction to detect clinical mastitis with automatic milking","volume":"70","author":"Kamphuis","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"10.3168\/jds.2026-28817_bib27","author":"Kingma"},{"key":"10.3168\/jds.2026-28817_bib28","first-page":"1","article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","volume":"18","author":"Li","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"10.3168\/jds.2026-28817_bib29","series-title":"Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA","first-page":"4765","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"issue":"OF","key":"10.3168\/jds.2026-28817_bib30","first-page":"671","article-title":"Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle","volume":"49","author":"Mammadova","year":"2015","journal-title":"Indian J. Anim. Res."},{"key":"10.3168\/jds.2026-28817_bib31","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of T4 phage lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochim. Biophys. Acta Protein Struct."},{"key":"10.3168\/jds.2026-28817_bib32","doi-asserted-by":"crossref","first-page":"2721","DOI":"10.3168\/jds.2024-25357","article-title":"Factors influencing somatic cell count and leukocyte composition in cow milk: A field study","volume":"108","author":"Mondini","year":"2025","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib33","doi-asserted-by":"crossref","first-page":"420","DOI":"10.3390\/vetsci12050420","article-title":"Neural network-aided milk somatic cell count increase prediction","volume":"12","author":"Nagy","year":"2025","journal-title":"Vet. Sci."},{"key":"10.3168\/jds.2026-28817_bib34","first-page":"543","article-title":"A method for unconstrained convex minimization problem with the rate of convergence O(1\/k2)","volume":"269","author":"Nesterov","year":"1983","journal-title":"Dokl. Akad. Nauk SSSR"},{"key":"10.3168\/jds.2026-28817_bib35","doi-asserted-by":"crossref","first-page":"4978","DOI":"10.3168\/jds.2022-22803","article-title":"Early detection of subclinical mastitis in lactating dairy cows using cow-level features","volume":"106","author":"Pakrashi","year":"2023","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib36","doi-asserted-by":"crossref","first-page":"4822","DOI":"10.3168\/jds.2020-19084","article-title":"Associations between differential somatic cell count and milk yield, quality, and technological characteristics in Holstein cows","volume":"104","author":"Pegolo","year":"2021","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0041-5553(64)90137-5","article-title":"Some methods of speeding up the convergence of iteration methods","volume":"4","author":"Polyak","year":"1964","journal-title":"USSR Comput. Math. Math. Phys."},{"key":"10.3168\/jds.2026-28817_bib38","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.3168\/jds.2007-0184","article-title":"Milking-to-milking variability for milk yield, fat and protein percentage, and somatic cell count","volume":"91","author":"Quist","year":"2008","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib39","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.3390\/ani11072131","article-title":"Predicting subclinical ketosis in dairy cows using machine learning techniques","volume":"11","author":"Sato\u0142a","year":"2021","journal-title":"Animals (Basel)"},{"key":"10.3168\/jds.2026-28817_bib40","doi-asserted-by":"crossref","first-page":"3959","DOI":"10.3168\/jds.2023-24243","article-title":"Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models","volume":"107","author":"Sato\u0142a","year":"2024","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib41","series-title":"Contributions to the theory of games","first-page":"307","article-title":"A value for n-person games","volume":"volume II","author":"Shapley","year":"1953"},{"key":"10.3168\/jds.2026-28817_bib42","doi-asserted-by":"crossref","first-page":"532","DOI":"10.3906\/vet-1607-78","article-title":"Detection of high levels of somatic cells in milk on farms equipped with an automatic milking system by decision trees technique","volume":"41","author":"Sitkowska","year":"2017","journal-title":"Turk. J. Vet. Anim. Sci."},{"key":"10.3168\/jds.2026-28817_bib43","author":"Smith"},{"key":"10.3168\/jds.2026-28817_bib44","author":"Snoek"},{"key":"10.3168\/jds.2026-28817_bib45","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.3168\/jds.2026-28817_bib46","doi-asserted-by":"crossref","first-page":"11489","DOI":"10.3168\/jds.2023-24513","article-title":"Evaluating the performance of herd-specific long short-term memory models to identify automated health alerts associated with a ketosis diagnosis in early-lactation cows","volume":"107","author":"Taechachokevivat","year":"2024","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.prevetmed.2023.105860","article-title":"Novel prediction models for hyperketonemia using bovine milk Fourier transform infrared spectroscopy","volume":"213","author":"Walleser","year":"2023","journal-title":"Prev. Vet. Med."},{"key":"10.3168\/jds.2026-28817_bib48","doi-asserted-by":"crossref","first-page":"5767","DOI":"10.3168\/jds.2007-0389","article-title":"Effects of breed, feeding system, and parity on udder health and milking characteristics","volume":"90","author":"Walsh","year":"2007","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib49","first-page":"830","article-title":"Interpretable machine learning models for early detection of subclinical mastitis using routine milk composition data","volume":"9","author":"Yal\u00e7in","year":"2025","journal-title":"ISPEC J. Agric. Sci."},{"key":"10.3168\/jds.2026-28817_bib50","doi-asserted-by":"crossref","first-page":"6863","DOI":"10.3168\/jds.2013-6846","article-title":"Effects of seasonal change and parity on raw milk composition and related indices in Chinese Holstein cows in northern China","volume":"96","author":"Yang","year":"2013","journal-title":"J. Dairy Sci."},{"key":"10.3168\/jds.2026-28817_bib51","doi-asserted-by":"crossref","first-page":"204","DOI":"10.3390\/ani16020204","article-title":"Machine learning and Shapley additive explanationbased interpretation for predicting mastitis in dairy cows","volume":"16","author":"Zhou","year":"2026","journal-title":"Animals (Basel)"}],"container-title":["Journal of Dairy Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022030226032339?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022030226032339?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T21:02:32Z","timestamp":1788555752000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022030226032339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":51,"alternative-id":["S0022030226032339"],"URL":"https:\/\/doi.org\/10.3168\/jds.2026-28817","relation":{},"ISSN":["0022-0302"],"issn-type":[{"value":"0022-0302","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Comparative analysis of artificial neural networks and classical machine learning models for predicting subclinical mastitis in dairy cows, with SHAP-based interpretation of predictions","name":"articletitle","label":"Article Title"},{"value":"Journal of Dairy Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.3168\/jds.2026-28817","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors.","name":"copyright","label":"Copyright"}]}}