Abstract: The analysis and processing of cattle disease data is increasingly important in modern veterinary medicine, especially with advances in big data and AI technologies. The integration of data analysis and mining techniques into animal husbandry enables the development of intelligent systems capable of diagnosing cattle diseases. This process begins with the collection of extensive electronic medical records from various sources, followed by thorough pre-processing to remove duplicates, remove stop words and segment words. Then use Eclat's algorithm to reveal correlations between specific diseases and their probabilities, which will lead to appropriate treatment decisions. Such a system allows for early treatment, minimizes loss of herders and promotes scientific progress in animal husbandry. As a real-time application, this concept could significantly help veterinarians manage cattle diseases more effectively. the system identifies connections between symptoms, disease types and treatment. The choice of the Eclat algorithm, known for its efficiency in pattern detection, enables fast processing of datasets. The implemented algorithm achieves approximately 96.5% accuracy, demonstrating its potential as a robust tool in veterinary medicine. The browser-based application is designed for cross-browser compatibility and accessibility for users in the medical field.
Keywords: Cattle Disease, Machine Learning, Data Science, Eclat Algorithm, Symptoms, Treatment.
|
DOI:
10.17148/IJARCCE.2024.13827
