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

Area of Science:

• Agricultural Science
• Data Science
• Machine Learning

Background:

• Multiple births in dairy farming negatively impact cow fertility, calf health, and farm profitability.
• Accurate classification of multiple birth data is crucial for optimizing dairy farm management.

Purpose of the Study:

• To evaluate the performance of various machine learning algorithms for classifying multiple birth data in dairy farming.
• To identify the most effective algorithms for predicting and managing multiple birth outcomes.

Main Methods:

• Utilized the Waikato Environment for Knowledge Analysis (WEKA) platform to assess 21 machine learning algorithms.
• Evaluated algorithm performance using metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Kappa statistic, classification accuracy, and computational time.

Main Results:

• Radial Basis Function (RBF) and Support Vector Machine (SVM) algorithms demonstrated superior performance, achieving the lowest error rates and highest accuracy.
• RBF model achieved a perfect Kappa statistic of 1 on the training set, indicating excellent classification.
• Algorithms like Voted Perceptron and Simple Cart showed lower accuracy, while K-nearest neighbours (KNN) and Random Forest offered a balance of accuracy and efficiency.

Conclusions:

• Algorithm selection is critical for robust and reliable classification of multiple birth data in dairy farming.
• RBF and SVM are highly recommended for dairy multiple birth data classification due to their high accuracy and reliability.
• Further research can explore algorithm trade-offs for specific farm management needs.