<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Annals of Science and Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Online veröffentlicht: 20. Juli 2019

Seitenbereich: 1 - 15

Eingereicht: 10. Nov. 2018

Akzeptiert: 03. Feb. 2019

DOI: https://doi.org/10.2478/ast-2019-0001

Schlüsselwörter
Blood Glucose Level, Diabetes Mellitus, Neuro-Fuzzy Network, Takagi-Sugeno, Type 1 Diabetes Mellitus

© 2019 N. O. Orieke et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 20. Juli 2019

Seitenbereich: 1 - 15

Eingereicht: 10. Nov. 2018

Akzeptiert: 03. Feb. 2019

DOI: https://doi.org/10.2478/ast-2019-0001

SchlüsselwörterBlood Glucose Level, Diabetes Mellitus, Neuro-Fuzzy Network, Takagi-Sugeno, Type 1 Diabetes Mellitus

© 2019 N. O. Orieke et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Blood Glucose Level, Diabetes Mellitus, Neuro-Fuzzy Network, Takagi-Sugeno, Type 1 Diabetes Mellitus