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

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 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.

RESULTS

The evaluation of the TBESO-BP model in the test phase reveals a coefficient of determination  = 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.

DISCUSSION

The 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.