TBESO-BP: an improved regression model for predicting subclinical mastitis
IntroductionSubclinical mastitis in dairy cows carries substantial economic, animal welfare, and biosecurity implications. The identification of subclinical forms of the disease is routinely performed through the measurement of somatic cell count (SCC) and microbiological tests. However, their accur...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Veterinary Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fvets.2025.1396799/full |
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| author | Kexin Han Yongqiang Dai Huan Liu Junjie Hu Leilei Liu Zhihui Wang Liping Wei |
| author_facet | Kexin Han Yongqiang Dai Huan Liu Junjie Hu Leilei Liu Zhihui Wang Liping Wei |
| author_sort | Kexin Han |
| collection | DOAJ |
| description | IntroductionSubclinical mastitis in dairy cows carries substantial economic, animal welfare, and biosecurity implications. The identification of subclinical forms of the disease is routinely performed through the measurement of somatic cell count (SCC) and microbiological tests. However, their accurate identification can be challenging, thereby limiting the opportunities for early interventions. In this study, an enhanced neural backpropagation (BP) network model for predicting somatic cell count is introduced. The model is based on TBESO (Multi-strategy Boosted Snake Optimizer) and utilizes monthly Dairy Herd Improvement (DHI) data to forecast the status of subclinical mastitis in cows.Materials and methodsThe Monthly Dairy Herd Improvement (DHI) data spanning from January 2022 to July 2022 (full dataset) was partitioned into both the training and testing datasets. TBESO addresses the challenge associated with erratic initial weights and thresholds in the BP neural network, impacting training outcomes. The algorithm employs three strategies to rectify issues related to insufficient population diversity, susceptibility to local optimization, and reduced accuracy in snake optimization. Additionally, six alternative regression prediction models for subclinical mastitis in dairy cows are developed within this study. The primary objective is to discern models by exhibiting higher predictive accuracy and lower error values.ResultsThe evaluation of the TBESO-BP model in the test phase reveals a coefficient of determination R2 = 0.94, a Mean Absolute Error (MAE) of 2.07, and a Root Mean Square Error (RMSE) of 5.33. In comparison to six alternative models, the TBESO-BP model demonstrates superior accuracy and lower error values.DiscussionThe TBESO-BP model emerges as a precise tool for predicting subclinical mastitis in dairy cows. The TBESO algorithm notably enhances the efficacy of the BP neural network in regression prediction, ensuring elevated computational efficiency and practicality post-improvement. |
| format | Article |
| id | doaj-art-986aaa518098480d96cfe6d1f9ff19fe |
| institution | DOAJ |
| issn | 2297-1769 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Veterinary Science |
| spelling | doaj-art-986aaa518098480d96cfe6d1f9ff19fe2025-08-20T02:53:54ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692025-04-011210.3389/fvets.2025.13967991396799TBESO-BP: an improved regression model for predicting subclinical mastitisKexin Han0Yongqiang Dai1Huan Liu2Junjie Hu3Leilei Liu4Zhihui Wang5Liping Wei6College of Information Science and Technology, Gansu Agricultural University, Lanzhou, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou, ChinaCollege of Veterinary Medicine, Gansu Agricultural University, Lanzhou, ChinaGansu Nongken Tianmu Dairy Co., Ltd., Jinchang, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou, ChinaIntroductionSubclinical mastitis in dairy cows carries substantial economic, animal welfare, and biosecurity implications. The identification of subclinical forms of the disease is routinely performed through the measurement of somatic cell count (SCC) and microbiological tests. However, their accurate identification can be challenging, thereby limiting the opportunities for early interventions. In this study, an enhanced neural backpropagation (BP) network model for predicting somatic cell count is introduced. The model is based on TBESO (Multi-strategy Boosted Snake Optimizer) and utilizes monthly Dairy Herd Improvement (DHI) data to forecast the status of subclinical mastitis in cows.Materials and methodsThe Monthly Dairy Herd Improvement (DHI) data spanning from January 2022 to July 2022 (full dataset) was partitioned into both the training and testing datasets. TBESO addresses the challenge associated with erratic initial weights and thresholds in the BP neural network, impacting training outcomes. The algorithm employs three strategies to rectify issues related to insufficient population diversity, susceptibility to local optimization, and reduced accuracy in snake optimization. Additionally, six alternative regression prediction models for subclinical mastitis in dairy cows are developed within this study. The primary objective is to discern models by exhibiting higher predictive accuracy and lower error values.ResultsThe evaluation of the TBESO-BP model in the test phase reveals a coefficient of determination R2 = 0.94, a Mean Absolute Error (MAE) of 2.07, and a Root Mean Square Error (RMSE) of 5.33. In comparison to six alternative models, the TBESO-BP model demonstrates superior accuracy and lower error values.DiscussionThe TBESO-BP model emerges as a precise tool for predicting subclinical mastitis in dairy cows. The TBESO algorithm notably enhances the efficacy of the BP neural network in regression prediction, ensuring elevated computational efficiency and practicality post-improvement.https://www.frontiersin.org/articles/10.3389/fvets.2025.1396799/fullsubclinical mastitissnake optimizationregression predictionneural networkDairy Herd Improvement |
| spellingShingle | Kexin Han Yongqiang Dai Huan Liu Junjie Hu Leilei Liu Zhihui Wang Liping Wei TBESO-BP: an improved regression model for predicting subclinical mastitis Frontiers in Veterinary Science subclinical mastitis snake optimization regression prediction neural network Dairy Herd Improvement |
| title | TBESO-BP: an improved regression model for predicting subclinical mastitis |
| title_full | TBESO-BP: an improved regression model for predicting subclinical mastitis |
| title_fullStr | TBESO-BP: an improved regression model for predicting subclinical mastitis |
| title_full_unstemmed | TBESO-BP: an improved regression model for predicting subclinical mastitis |
| title_short | TBESO-BP: an improved regression model for predicting subclinical mastitis |
| title_sort | tbeso bp an improved regression model for predicting subclinical mastitis |
| topic | subclinical mastitis snake optimization regression prediction neural network Dairy Herd Improvement |
| url | https://www.frontiersin.org/articles/10.3389/fvets.2025.1396799/full |
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