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@article{196200,
        author = {Sakshi Sanjay Gandhi and Sanika Narendra Gunge and Prof.Padir J.D},
        title = {Diabetese-Detection-Using-Machine-Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2338-2342},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196200},
        abstract = {Diabetes is a progressive metabolic disorder characterized by elevated blood glucose levels caused by insulin resistance or insufficient insulin production. Early identification of individuals at high risk can significantly reduce long-term complications such as cardiovascular diseases and neuropathy. This research proposes an intelligent diabetes risk prediction system based on ensemble learning and feature optimization techniques. The system utilizes clinical parameters including glucose concentration, BMI, age, blood pressure, insulin level, and diabetes pedigree function. Advanced preprocessing techniques such as outlier detection and feature correlation analysis are applied to enhance model performance. Multiple classifiers including K-Nearest Neighbors (KNN), Gradient Boosting, and Random Forest are implemented and compared. Experimental findings reveal that Gradient Boosting achieved the highest prediction accuracy of 94.1%. The proposed framework provides a reliable and scalable solution for early diabetes screening.},
        keywords = {},
        month = {April},
        }