## 2 Related Works
There are other methods using the same sort of techniques, for example, Gaussian Processes (GP) with Radial Basis Function Kernels (RBF) [19], which permit overall uniformity and limitless levels of basic functions. However, these are not often employed, although some researchers have used such techniques and the results have shown promise [20]. Some recent research has also looked at GP [21], examining the potential for automatic insulin delivery that could reduce the number of hypoglycemic events. GP is a non-parametric methodology that revolves around creating a model of observable responses from a number of points in the training data (function values) and using them as multivariate normal random variables [22]. It is assumed that the function values will be distributed in such a way that the function will operate smoothly. Specifically, when closeness exists (in an Euclidean distance context) between matching input vectors that decay with divergences, the two function values will be closely correlated. Employing an assumed distribution by applying a basic probability manipulation allows for posterior distribution of hitherto unpredicted function values. RF algorithms use a method referred to as bagging, which re-samples data instances a number of times in order to create several training subsets on the same training data [23]. A decision tree is then designed for every training subset until a tree ensemble has been built. Every tree then inputs a unit vote influencing the outcomes of the incoming data instance cost label. Xu [24] diagnosed DM1 employing RS with a public hospital dataset, and this methodology performed better (85% success) than a number of other methodologies such as the ID3 (Iterative Dichotomiser 3) algorithm (78.57%), the naive Bayes algorithm (79.89%), and the AdaBoost algorithm (84.19%). SVM is a dual learning algorithm that undertakes example processing just by computing the dot-product. It is possible to efficiently compute such dot-products between feature vectors, employing a kernel function that does not obliterate every corresponding feature. Once the SVM learner has been supplied with the kernel function, it seeks out a hyperplane that can separate negative and positive examples and simultaneously maximize the size of their separation (margin). SVM is not prone to over-fitting and performs well in generalization as a result of the max-margin criterion employed throughout optimization. Although MLP solutions may solely be a local optimum, SVMs will always converge to a global optimum as a result of corresponding convex optimization formulations. The work [25] offered a useful method of hypoglycemic detection based on SVM, employing a galvanic skin response using skin temperatures, monitoring of heart rates, and a small band. Regrettably, the size and type of the dataset used in this research has unintentionally limited the applicability of the results. SVM revolves around employing high-dimensional feature spaces (built using transformational original variables) and the application of penalties to the resulting complexities by using a penalty term integrated within the error function [26]. Other approaches like the stacking-based General Regression Neural Network (GRNN) ensemble model are truly promising [27; 28; 29], but it has not been previously applied to DM1. In this sense, the authors of this work intend to analyze it in future research. In relation to the foregoing, it can be concluded that these techniques have shown promise in terms of forecasting the dynamics of glycaemia. Nevertheless, as far as the authors are aware, no research has yet been undertaken employing selection techniques and a variety of forecasting algorithms utilizing real-world data and a proper feature set to reveal the most accurate of the options available.