Transformative Approaches in Diabetes Management Using Non- Invasive Glucose Monitoring and Machine Learning-Based Prediction Models

Diabetes is a global health concern that poses significant management and early detection challenges, as traditional monitoring techniques are often invasive and inconvenient. This paper explores advancements in non-invasive blood glucose monitoring systems that leverage sensor technologies and machine learning methods to enhance diabetes care. Non-invasive systems utilize photoplethysmography, electrocardiography, and spectroscopy, offering user-friendly solutions for real-time glucose monitoring. Machine learning algorithms, including Logistic Regression, Random Forests, Support Vector Machine, and XGBoost, are involved in this work for the prediction of diabetes. Gradient Boosting emerges by achieving an accuracy of 99.83% and demonstrating high stability. The study highlights the potential for developing innovative diabetes management solutions by combining non-invasive glucose monitoring systems, wearable technologies, and machine learning models, ultimately enhancing glycemic control, facilitating early diagnosis, and personalizing treatment. The findings highlight the need for further research and clinical validation to address limitations and ensure widespread usability.