Development of a Predictive Model for Fowl-Cholera Infection Status in Poultry Using Advanced Data Mining Analysis Techniques and Logistic Regression Modeling.

Fowl cholera, caused by Pasteurella multocida, remains one of the most economically devastating poultry diseases worldwide. Rapid and accurate diagnosis is critical for effective intervention, yet traditional methods often fall short in speed and predictive accuracy. This study presents a Big Data-driven data mining approach to diagnose fowl cholera in poultry, leveraging a dataset of 500 samples characterized by variables such as bird age, vaccination history, environmental conditions, clinical symptoms, and mortality rates. Machine learning algorithms including Logistic Regression, Random Forest, and Gradient Boosting were deployed to model disease prediction, with Random Forest achieving the highest accuracy at 94.6%. Data preprocessing techniques, feature selection, and cross-validation were applied to ensure robustness and scalability. The findings demonstrate that environmental factors, vaccination gaps, and bird age are among the most significant predictors. This research highlights the transformative potential of Big Data and advanced data mining in veterinary epidemiology, providing a scalable diagnostic framework for poultry health management.

Fowl Cholera, Poultry Disease Prediction, Logistic Regression, Data Mining, Predictive Analytics

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