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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Optimization of the different prediction techniques for mastitis disease detection in cows

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pp. 1519–1538Vol. 45Issue 6September 2024DOI: 10.47974/JIOS-1631XML
Received:
08 Apr 2024
Published Online:
30 Sep 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1631
Pages:
1519–1538

Abstract

The pervasive and economically impactful disease in dairy animals, mastitis, presents a significant challenge to the global dairy industry. Accurate and prompt diagnosis is paramount for effective treatment and to curb the spread within the herd. Leveraging advancements in machine learning for disease identification, this article reviews the application of ensemble models in detecting mastitis in cows. The document outlines mastitis, covering its origins, symptoms, and implications for milk supply and animal well-being, emphasizing the limitations of traditional diagnostic methods. It underscores the need for automated, reliable detection strategies and offers a thorough assessment of ensemble machine learning models consists of random forest, gradient boosting, bagging, and stacking. The evaluation scrutinizes model performance, considering datasets, feature selection methods, model designs, and assessment metrics. This review serves as a valuable resource for researchers, veterinarians, and dairy industry professionals seeking to implement machine learning algorithms for mastitis identification. Providing insights into the current landscape pertaining to cow mastitis worldwide , identifying knowledge gaps, and proposing solutions for enhanced accuracy, the review contributes to advancing mastitis detection, ultimately improving cow health and productivity.

Keywords

Mastitis detectionEnsemble machine learningCowsSymptomsDiagnosis

Subject Classifications

68T0592B2092D50

References

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