Abstract: Analysis and processing of cattle disease data to extract meaningful insight is a complex and challenging function in today's veterinary and agricultural fields. With rapid progress in large data and artificial intelligence, data analysis and mining have become rapidly important in animal husbandry. This system takes advantage of the large-scale, multi-source electronic medical records (EMRs) of cattle and applies data analysis and mining techniques to create a wise diagnosis system for cattle diseases. The procedure for preparing raw EMR data for the process begins with broader text preprocessing, including Diduplication, Stop Word Removal, and Word Segmentation. Subsequently, the ECLAT algorithm is employed to identify correlations between the symptoms , diseases, eventually suggested appropriate treatment plans. It enables timely diagnosis and treatment, reduces economic losses for herds and promotes scientific, intelligent methods in livestock management. Machine Learning algorithm is used to highlight the pattern and extract proceeding from cattle disease dataset. This concept can be extended to a real -time application designed to help veterinary doctors in effectively managing cattle health. The system uses ECLAT algorithm to establish a correlation between symptoms, disease types and treatment, offering data -powered approaches to veterinary care.

Keywords: Cattle Disease Prediction, Symptom - Disease Correlation, Machine Learning, ECLAT Algorithm, Pattern Discovery

| DOI: 10.17148/IARJSET.2025.125342