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@article{191968,
        author = {Padir.J.D and Sakshi Sanjay Gandhi and Sanika Narendra Gunge},
        title = {Diabetese-Detection-Using-Machine-Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8654-8657},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191968},
        abstract = {Diabetes mellitus is a chronic metabolic disorder that affects millions of people worldwide and is one of the leading causes of cardiovascular disease, kidney failure, blindness, and nerve damage. Early diagnosis of diabetes is essential for reducing health risks and improving patient outcomes. This project presents a machine learning-based diabetes detection system that predicts the presence of diabetes using medical attributes such as glucose level, blood pressure, insulin level, BMI, age, and skin thickness. The proposed system employs multiple classification algorithms including Logistic Regression, Support Vector Machine (SVM), and Random Forest to analyze patient data and generate accurate predictions. Experimental evaluation shows that the Random Forest model achieves the highest performance with an accuracy of 92%. The system provides a fast, cost-effective, and automated solution that can assist healthcare professionals in early diagnosis and decision-making.},
        keywords = {},
        month = {January},
        }