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@article{182940,
        author = {Geethanjali M V},
        title = {Symptom-Driven Predictive Diagnosis and Treatment of Cattle Health Issues},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3887-3992},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182940},
        abstract = {Analyzing data on cattle diseases and processing it to obtain relevant information. Data mining and analysis are being employed in animal husbandry more and more due to the big data and artificial intelligence fields rapid development. This system collects data from numerous sources for electronic medical records on cattle, and it uses data mining and analytical technologies to create an intelligent system for diagnosing diseases in cattle. Initially, stop words, word segmentation, and repetition in the cattle electronic medical record data are eliminated using text preprocessing technology. The corresponding treatment plan will be determined by correlating the specific disease name and probability using various unsupervised learning algorithms, in addition to efficiently reducing herder losses and promoting the advancement of scientific intelligence in animal husbandry, the system can promptly treat illnesses. To process the datasets on cattle diseases and extract relevant medical patterns and information, machine learning methods are employed. We can create a real-time program based on this notion that will help veterinarians treat cattle illnesses more effectively. Through the application of data science approaches, the system determines the relationship between the symptoms, types and treatments of cattle diseases. We employ to create a browser-based application that is suitable for many browser versions and types, specifically for the medical field. To avoid all of these issues, a system that automatically ascertains the links between illnesses, symptoms, and therapies is needed to find the patterns. This improves the efficacy of the method for determining patterns of sickness in cattle.},
        keywords = {Cattle disease, Unsupervised learning, Machine Learning, Data Science, Eclat, SFIT, Apriori TID, Symptoms, Treatments.},
        month = {July},
        }