An Analytical Predictive Model and Secure Wed Based Personalized Diabetes Monitoring System using Stacking Ensemble Classification

Abstract

As the number of people diagnosed with diabetes continues to rise, this study takes a groundbreaking approach by developing a secure web-based personalized diabetes monitoring system that incorporates analytical prediction models. This groundbreaking technology was developed in response to the critical need for sophisticated monitoring solutions that address the unique demands of each patient. The suggested method aims to transform diabetes treatment by using predictive modeling to anticipate diabetic trends and possible consequences. The study demonstrates a strong commitment to security by creating a web-based platform that handles patient data with the highest level of care. By resolving important privacy problems and creating a trustworthy environment for users, this secure framework guarantees the protection of sensitive health information. This approach takes use of several models' characteristics to make diabetes trend estimates more accurate and reliable. In addition to improving the system's prediction skills, stacking ensemble classification helps it adapt to different patient profiles. Because of the importance of accessibility and usability in encouraging patient participation, the suggested solution revolves around the creation of a user-friendly online interface. The interface is a living, breathing platform that allows for frictionless communication between healthcare practitioners and their patients. With the help of tailored insights and trend predictions, patients are better able to manage their diabetes. At the same time, doctors and hospitals have access to all the patient information they need, which allows them to take better, more preventative measures.