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

N. O. Orieke, O. Asaolu, T. Fashanu, O. Fasanmade
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Abstract

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.
胰岛素-膳食效应联合神经模糊模型预测1型糖尿病患者血糖水平
糖尿病是一种影响人体合理利用和调节葡萄糖能力的代谢紊乱。它在世界范围内普遍存在,但管理起来却很脆弱,成本很高。糖尿病也很难建模,因为它是非线性的,动态的,并且充满了患者特有的不确定性。建立了一种利用胰岛素和膳食耦合效应预测1型糖尿病患者血糖水平的神经模糊模型。本研究确立了预测1型糖尿病患者血糖水平的必要和充分条件是:了解患者在不同代谢情景下的胰岛素效应和膳食效应,以及胰岛素和膳食效应的透明耦合。神经模糊模型是用从一个1型糖尿病患者收集的两个月的数据来训练的。模型的Clarke 's Error Grid Analysis (CEGA)显示,在4小时的预测窗口内,87.5%的预测落在A区,其余12.5%的预测落在B区。该模型揭示了胰岛素和葡萄糖反应随着患者身体质量指数(BMI)的变化而发生的显著变化。
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