## 1 Introduction
New wearable electronic technology may be a significant addition to the management scheme, providing a totally novel new prospect for managing diabetes and significantly improving the forecast accuracy of glucose levels. There are many forms of wearable technology that could be used for monitoring not only glucose levels but also a variety of physiological elements. Examples include taking electrocardiographs, monitoring heart rate, registering activity and exercise levels, and monitoring interstitial/blood glucose levels. There is a considerable amount of wearable technology that can register an individual's daily activities and physiological condition many times each minute. Using such resources, we have developed a full dataset of biomedical features related to glycaemia. We harvested these by undertaking an in-depth passive-monitoring program in the real world with 25 DM1 patients, helping us to effectively identify patterns which allow us to model the crucial elements of glucose levels. With these data, we can then develop an accurate glycaemia forecast. Some very successful MPC-based solutions for glycaemia prediction, which make use of microcontrollers, have been presented in the literature [8]. Although time series values-prediction is well-trodden in the field of Machine Learning (ML), it is common to perform appropriate feature selection such as pre-processing, in order to enhance the efficacy of the predictive algorithm. Many of these predictive algorithms have only been applied to blood glucose prediction, but to the best of the authors' knowledge, there has been no comparative study not only of the prediction techniques, but also of the feature selection techniques. We find that it is complex to make fair comparisons with their results since each one uses different databases, with different added features, evaluating them in different ways (at different predictive horizons, different error evaluation metrics, etc.) and throughout different monitoring times. With all this, it is complicated to reach a conclusion on which one behaves better and it is not possible to extract overall conclusions. In addition, the previous phase of variable selection has not been compared either, so the influence of this on the improvement of the prediction has not been studied. The aim of the paper is therefore to make a fair comparison of these algorithms with a complete database. Moreover, the inclusions of the variables collected in our database are rarely seen in studies on blood glucose prediction. Thus, in this paper, a rich and complete dataset has been acquired alongside a novel process of features selection that enhances the performance and comparison of the prediction has been developed. We believe that this paper will enable a confident combination of feature selection and forecasting techniques to achieve more precise predictions in an assumable predictive horizon (PH). In short, the main contributions of this paper are as follows:  
* A brief literature review on variable selection and prediction methods for glycaemia values in diabetics. * To use an innovative database in the field of DM1, both in terms of the number of patients/variables considered and the monitoring time covered. * To test different variable selection techniques. * To combine these feature selection techniques with different predictive algorithms. * To discuss the influence of the variable selection techniques on the performance of the predictive algorithm, as well as to study the accuracy achieved. This research has employed a number of cutting-edge modeling and forecasting techniques. Implementation and analysis have been undertaken using six feature selection techniques alongside four forecasting techniques. The paper is organized as follows: Section 2 describes some previous works on forecasting glycaemia in DM1 to frame our research. In Section 3, we present the feature selection techniques and predictive algorithms.