Real-Time Glucose Level Interpretation Using a Fuzzy Logic Framework for Diabetes Management
Bibliographic record
Abstract
Successful control of diabetes necessitates continuous observation of blood glucose levels and timely intervention to prevent acute complications. Traditional threshold-based systems often fail to capture subtle glucose fluctuations, particularly in real time. This paper presents a fuzzy logic-based system for dynamically assessing diabetes status and determining insulin doses using real-time glucose data from wearable or handheld sensors. Using expert-defined linguistic variables and fuzzy membership functions, the model categorizes glucose levels into clinically meaningful states, such as hypoglycemia, normoglycemia, and hyperglycemia, with graded severity. The fuzzy inference engine generates personalized alerts and dose recommendations based on American Diabetes Association (ADA) guidelines, ensuring medical relevance. The system was implemented using Python and tested across a wide glucose range (40–310 mg/dL). Simulation results showed that the model accurately recommended 0 units at low glucose levels (50–65 mg/dL), small doses at borderline values, and aggressive dosing at critical levels, with smooth transitions between categories. Compared to traditional PID control, the fuzzy logic model offered safer, more conservative dose adjustments and reduced risk of overcorrection. Designed for integration into mobile health platforms and intelligent agents like Furhat, this model represents a major step forward in delivering autonomous, interpretable, and patient-centric diabetes care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".