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@article{197904,
        author = {Okardi Biobele and Henry Uzor Okeoghene},
        title = {Diabetes Prediction Model Using Machine Language},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11219-11228},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197904},
        abstract = {This diabetes detection project leveraged the Support Vector Machine (SVM) algorithm with a dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, sourced from Kaggle's repository of community-published data for machine learning projects. Agile methodology, supported by essential Python libraries within the Anaconda IDE, facilitated flexible model development. A Streamlit web interface enabled easy data predictions and feature selection. Its significance lies in providing a reliable tool for early diabetes prediction, improving patient outcomes. The SVM's effectiveness with complex datasets and Agile's continuous refinement enhance accuracy. This approach combines Python, Streamlit's web interface, and SVM for effective early diabetes prediction, offering substantial benefits to healthcare efficiency and patient care.},
        keywords = {},
        month = {April},
        }