Apriori-Based Antibiotic Association Rule Mining for Optimized Mastitis Treatment in Dairy Cattle

Mastitis is a common and economically significant disease in dairy cattle, necessitating effective antibiotic treatment for optimal management. This study leverages association rule mining and frequent itemset analysis-core techniques in data analytics - to identify the most effective antibiotic combinations for treating mastitis in cows and buffaloes. By analyzing large-scale veterinary data, we extract key associations between antibiotics and assess their efficacy using statistical methods, including t-tests and chi-square tests. The findings provide a computational framework for enhancing evidence-based decision-making in healthcare analytics, demonstrating the potential of AIdriven methodologies in advancing antimicrobial stewardship and optimizing therapeutic outcomes for veterinary practitioners.