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Intelligent Dietary Guidance System for Diabetes Patients: A Web-Based Solution
Abstract
Diabetes management requires continuous monitoring of blood glucose levels and careful regulation of dietary intake. This chapter presents a web-based intelligent dietary guidance system designed to support patients in making informed food choices. The system collects patient-specific information, including body mass index (BMI), pre- and post-meal glucose values, and detailed meal composition. A machine learning model processes these inputs to predict postprandial glucose change (ΔBG) and classify whether a meal poses a risk of hyperglycemia. Personalized recommendations are then generated to guide patients toward safer meal planning. The system is implemented as a three-tier architecture consisting of a frontend interface, a backend prediction engine, and a secure database. Evaluation results indicate that the predictive model achieves clinically relevant accuracy, with mean absolute error (MAE) of approximately 12 mg/dL and a classification ROC AUC of 0.86. Visualization panels, including glucose trend charts and nutrient composition diagrams, enhance interpretability for both patients and clinicians. Integration with web technologies ensures accessibility, scalability, and potential interoperability with continuous glucose monitoring devices and electronic health record systems. This work demonstrates how intelligent web-based systems can improve diabetes self-management by delivering personalized, data-driven dietary recommendations. It also highlights future directions for expanding datasets, incorporating contextual lifestyle factors, and validating the system through clinical trials.
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