Data Mining Strategies for Understanding Birth Patterns in Dairy Cattle: Single and Multiple Birth Analysis

Data mining is a robust methodology designed to predict feature performance, identify trends within datasets, classify data based on similarities and optimize resource allocation. In the context of dairy farming, multiple birth is generally regarded as an undesirable outcome due to its adverse effects on cow fertility rates and the health of multiple calves, ultimately impacting farm profitability. This study conducts a comprehensive evaluation of various machine learning algorithms for the classification of multiple birth data using the Waikato Environment for Knowledge Analysis (WEKA) platform. The performance of 21 algorithms was evaluated using multiple metrics, including mean absolute error (MAE), root mean square error (RMSE), Kappa statistic, classification accuracy and computational time. The results reveal significant variability in the performance of the algorithms. Notably, the radial basis function (RBF) and support vector machine (SVM) algorithms outperformed others, achieving the lowest MAE and RMSE values, along with the highest Kappa statistics and classification accuracy on both training and test datasets. Specifically, the RBF model achieved a perfect Kappa statistic of 1 on the training set, indicating flawless agreement between predicted and actual classifications. In contrast, algorithms such as Voted Perceptron and Simple Cart exhibited higher error rates and lower classification accuracy, rendering them less suitable for this task. The analysis also highlighted trade-offs between accuracy and computational efficiency, with algorithms like K-nearest neighbours (KNN) and Random Forest providing a balanced performance. These findings emphasize the critical importance of selecting appropriate algorithms and evaluation metrics to achieve robust and reliable classification outcomes in multiple birth data analysis.