TBESO-BP: an improved regression model for predicting subclinical mastitis.

ABSTRACT:

Introduction

Subclinical 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 methods

The Monthly Dairy Herd Improvement (DHI) data spanning from Janua

SUBMITTER: Han K 

PROVIDER: S-EPMC11997978 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature