On the Possibility of Predicting Glycaemia ‘On the Fly’ with Constrained IoT Devices in Type 1 Diabetes Mellitus Patients

Abstract

Type 1 Diabetes Mellitus (DM1) patients are used to checking their blood glucose levels several times per day through finger sticks and, by subjectively handling this information, to try to predict their future glycaemia in order to choose a proper strategy to keep their glucose levels under control, in terms of insulin dosages and other factors. However, recent Internet of Things (IoT) devices and novel biosensors have allowed the continuous collection of the value of the glucose level by means of Continuous Glucose Monitoring (CGM) so that, with the proper Machine Learning (ML) algorithms, glucose evolution can be modeled, thus permitting a forecast of this variable. On the other hand, glycaemia dynamics require that such a model be user-centric and should be recalculated continuously in order to reflect the exact status of the patient, i.e., an ‘on-the-fly’ approach. In order to avoid, for example, the risk of being disconnected from the Internet, it would be ideal if this task could be performed locally in constrained devices like smartphones, but this would only be feasible if the execution times were fast enough. Therefore, in order to analyze if such a possibility is viable or not, an extensive, passive, CGM study has been carried out with 25 DM1 patients in order to build a solid dataset. Then, some well-known univariate algorithms have been executed in a desktop computer (as a reference) and two constrained devices: a smartphone and a Raspberry Pi, taking into account only past glycaemia data to forecast glucose levels. The results indicate that it is possible to forecast, in a smartphone, a 15-min horizon with a Root Mean Squared Error (RMSE) of 11.65 mg/dL in just 16.15 s, employing a 10-min sampling of the past 6 h of data and the Random Forest algorithm. With the Raspberry Pi, the computational effort increases to 56.49 s assuming the previously mentioned parameters, but this can be improved to 34.89 s if Support Vector Machines are applied, achieving in this case an RMSE of 19.90 mg/dL. Thus, this paper concludes that local on-the-fly forecasting of glycaemia would be affordable with constrained devices.

Machine Learning Approach for Postprandial Blood Glucose Prediction in Gestational Diabetes Mellitus

“…There are methods showing good accuracy in predicting BG based on preceding CGM records, such as methods utilizing convolutional neural networks [24], [25], which shown RMSE of 1.21 (for the best patient) and 1.85 mmol/L (in average) respectively for BG prediction 60 minutes ahead, which are good values concerning prediction for type 1 diabetes patients). The comparable result was shown recently utilizing random forest in the same setting [26]. But the requirement of CGM systems to be constantly utilized in order to predict BG is expensive and inapplicable in a wide clinical practice for GDM patients.…”

“…All the models exhibit adequate accuracy that allows them to be used in patient assistance. The developed model in comparison to others does not require microbiome data as models by Zeevi et al [21] and Mendes-Soares et al [22] or continuous blood glucose measurements on the time of prediction as models by Li et al [24], Zhu et al [25] and Rodriguez-Rodriguez et al [26], which makes it much more accessible for clinical practice.…”

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

“…This approach fixes the over-fitting problem of decision trees. Seo et al [ 43 ], Güemes et al [ 60 ], Vahedi et al [ 33 ], G Noaro et al [ 72 ], Vu et al [ 47 ], Reddy et al [ 40 ], Chen et al [ 30 ], Dave et al [ 52 ], Calhoun et al [ 45 ], Amar et al [ 75 ], Hidalgo et al [ 77 ], and Rodriguez et al [ 79 ] have all used RF for predicting/detecting hypoglycemia. Ruan et al [ 31 ] and Cappon et al [ 66 ] used the XGboost algorithm.…”

“…When a test point is brought to the model, SVM maps it to one of the various categories and then assigns it a label. Marling et al [ 32 ], Mosquera-Lopez et al [ 48 ], Seo et al [ 43 ], Güemes et al [ 60 ], Oviedo et al [ 68 ], Vehi et al [ 59 ], Chen et al [ 30 ], Bertachi et al [ 41 ], and Rodriguez et al [ 79 ] have all used SVM.…”

A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

“…Gaussian Processes (GPs) with Radial Basis Function Kernels (RBF) [52] and other forms of comparative strategy create consistency overall and allow for a limitless quantity of basic functions, but these are rarely used, even though some past research has demonstrated that it can show promise [53].…”