A Transparent Rule-Based Decision-Support Prototype for Diabetic Menu Planning and Meal-Combination Optimization: Development and Computational Evaluation

Background/Objectives: Translating medical nutrition therapy guidance into practical meal plans requires consistent application of clinical and dietetic rules while accommodating different therapeutic regimens. This study developed and computationally evaluated a transparent Python-based decision-support prototype for diabetic menu planning.

Methods

The prototype combines procedural decision rules, food-exchange patterns, nutritional checks, and mixed-integer linear programming (MILP). Two meal-frequency pathways were represented: a conventional multi-meal pathway and a three-main-meal pathway for people treated with modern basal–bolus insulin regimens. For the multi-meal pathway, a structured dataset containing 42 candidate meals (seven alternatives in each of six meal categories) was used for objective-specific optimization and exhaustive evaluation of all 117,649 possible daily combinations under predefined nutritional constraints. Sensitivity to the allowable energy deviation and trade-offs among nutritional objectives were also examined.