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@article{180099,
        author = {krati and ansh jindal and Dr. M. Altamash Sheikh},
        title = {A Machine Learning-Powered Framework for Diabetes Prediction with Real-Time Web Deployment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {154-159},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180099},
        abstract = {The rise of machine learning in healthcare has 
revolutionized predictive analytics, particularly in the 
early detection of chronic diseases like diabetes. The 
model was developed using a Support Vector Machine 
(SVM) algorithm, trained specifically on the Pima 
Indians dataset to identify patterns linked to diabetes 
occurrence. 
The model undergoes systematic 
preprocessing, which includes data normalization and 
outlier removal to boost accuracy. A custom-built 
Streamlit application provides a user-friendly interface, 
enabling real-time diabetes risk predictions. Future 
development aims to enhance diagnostic precision by 
integrating continuous health monitoring and 
diversified data sources.},
        keywords = {Diabetes risk assessment, predictive  healthcare, machine learning, support vector machine,  Streamlit application, feature engineering.},
        month = {May},
        }