A Coupled Insulin and Meal Effect Neuro-Fuzzy Model for The Prediction of Blood Glucose Level in Type 1 Diabetes Mellitus Patients

• Published in: Annals of science and technology Vol. 4; no. 1; pp. 1 - 15
• Main Authors: Orieke, N. O., Asaolu, O.S., Fashanu, T. A., Fasanmade, O. A.
• Format: Journal Article
• Language: English
• Published: Sciendo 01.06.2019
• Subjects: Blood Glucose Level; Diabetes Mellitus; Neuro-Fuzzy Network; Takagi-Sugeno; Type 1 Diabetes Mellitus
• ISSN: 2544-6320; 2544-6320
• DOI: 10.2478/ast-2019-0001

Diabetes Mellitus is a metabolic disorder that affects the ability of the human body to properly utilize and regulate glucose. It is pervasive world-wide yet tenuous and costly to manage. Diabetes Mellitus is also difficult to model because it is nonlinear, dynamic and laden with mostly patient specific uncertainties. A neuro-fuzzy model for the prediction of blood glucose level in Type 1 diabetic patients using coupled insulin and meal effects is developed. This study establishes that the necessary and sufficient conditions to predict blood glucose level in a Type 1 diabetes mellitus patient are: knowledge of the patient’s insulin effects and meal effects under diverse metabolic scenarios and the transparent coupling of the insulin and meal effects. The neuro-fuzzy models were trained with data collected from a single Type 1 diabetic patient covering a period of two months. Clarke’s Error Grid Analysis (CEGA) of the model shows that 87.5% of the predictions fall into region A, while the remaining 12.5% of the predictions fall into region B within a four (4) hour prediction window. The model reveals significant variation in insulin and glucose responses as the Body Mass Index (BMI) of the patient changes.