Abstract
Engineering smart software that can monitor, predict, and control blood glucose is critical to improving patients' quality of treatments with type 1 Diabetic Mellitus (T1DM). However, ensuring a reasonable glycemic level in diabetic patients is quite challenging, as many methods do not adequately capture the complexities involved in glycemic control. This problem introduces a new level of complexity and uncertainty to the patient's psychological state, thereby making this problem nonlinear and unobservable. In this paper, we formulated a mathematical model using carbohydrate counting, insulin requirements, and the Harris-Benedict energy equations to establish the framework for predicting and controlling blood glucose level regulation in T1DM. We implemented the framework and evaluated its performance using root mean square error (RMSE) and mean absolute error (MAE) on a case study. Our framework had less error rate in terms of RMSE and MAE, which indicates a better fit with reasonable accuracy.
Highlights
In recent decades, the impact of data science and communication technology in several domains– the healthcare domain– has been profound to an unprecedented level (Chen et al, 2016)
Our objective is to improve the accuracy of control and prediction of blood glucose (BG) levels in patients suffering from type 1 diabetic mellitus (T1DM) to avoid the occurrence of hypoglycemia and hyperglycemia
In our research, we introduced the need for a requirements engineering (RE) approach to gain adequate insight into what is expected to give patients with T1DM better care with the use of programmable controllers
Summary
The impact of data science and communication technology in several domains– the healthcare domain– has been profound to an unprecedented level (Chen et al, 2016).
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