## 2 Related Works  
As mentioned in Section 1, it is crucial that an accurate range of variables and effective data collection methodology is employed when predicting blood glucose levels and that note is taken in the way they have previously been used. These variables must be related as time-series data, as the level of historical occurrences is considered as significant. Feature selection using a time-series is not the same as feature selection using standard data. With standard static data, target values only refer to the features' contemporary values. However, with feature selection in time-series, the target values are related to feature values at various points in the past as well as the contemporary value. This means that it is essential in feature selection in time-series to excise redundant/irrelevant variables and features, and selecting the relevant previous values for the creation of an effective dataset. In order to forecast glucose levels, it is widely recognized that the effect of previous values has importance. One example is the research of Eskaf et al. [9] who found, by employing discrete Fourier transformations, that blood glucose levels vary as a result of meals within a single timeframe of eating. This means that there is the capacity (which is crucial) to select the correct influential variables and so reduce the dimensionality. This is an important stage in processing [10] prior to applying a data mining algorithm to any dataset. Feature selection methodologies with time-series can be separated into filter, wrapper, and embedded techniques [11]. Filter techniques simply use the data in deciding on the features for retention. Wrapper techniques employ a learning algorithm wrapped around the feature search, selecting the features on the basis of its performance. With embedded methods, weighting is employed to control parameter values. In all cases, applications of variable selection methods can affect the course of diabetes mellitus. Balakrishnan et al. [12] applied Support Vector Machines (SVM) to rank variables affecting type 2 diabetes, and some hybrid methods have been proposed to optimize the diagnosis of diabetes [13]. However, there have not been many studies that apply variable selective methods to features that affect the immediate course of blood glucose in patients with type 1 DM. This could be due to some variables only recently being considered for predicting blood sugar [14]. In 2019, a sequential backward selection algorithm was successfully applied using a linear model in a cross-validation (CV) setting, obtaining an optimized and reduced subset [15]. Despite this, a study comparing the performance of different feature selection methods applied to diabetic patients' glycaemia has not been developed. Numerous attempts have been made to develop a reliable prediction of glucose in DM1 patients. In this case, there have been approaches from a univariate point of view [16], using Autoregressive Integrated Moving Average (ARIMA), Random Forest (RF) and Support Vector Machines (SVM) with acceptable results. However, although univariate approximations can be interesting in computationally restricted environments, multivariate methods have demonstrated higher accuracy [17]. In this regard, some forecasting strategies need to be highlighted where results will be improved after a proper feature selection preliminary stage. Linear Regression (LR) is probably the simplest methodology. This group of models endeavors to discover an assessment of the parameters of the model with the goal that the summation of the squared errors is minimized. Although it is the simplest, its accuracy could be sufficient, and due to its simplicity, it is easily executable even by limited hardware. In any case, recent approximations using Least Absolute Shrinkage and Selection Operator (LASSO) regression have achieved acceptable accuracy and good performance [18].