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@article{172364,
        author = {AMREEN FIRDOSE A and J.DIVYA LAKSHMI and MANU R and RACHANA R K and VARSHINI S},
        title = {NON-INVASIVE DIABETES DETECTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {2997-3002},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172364},
        abstract = {Diabetes, a chronic metabolic disorder, requires early and accurate diagnosis for effective management. This paper presents a non-invasive diabetes detection framework using Photoplethysmography (PPG) signals from the MAX30100 sensor. Features such as heart rate, oxygen saturation, and patient-specific metrics like age, BMI, and blood pressure are processed by machine learning models, including Logistic Regression and XGBoost, for classification. The system integrates real-time data collection, cloud-based processing via ThingSpeak, and a user-friendly web interface for visualization. This approach reduces reliance on invasive procedures and offers a scalable, cost-effective solution for early diabetes detection.},
        keywords = {},
        month = {January},
        }