## 7 Conclusions and Future Work  
Due to innovative breakthroughs in technology, our global outlook continues to transform in many extraordinary ways. Consequently, life has become easier in many respects, thanks to many enhancements that can be considered ground-breaking. With the task of reaching an extensive and definitive means of regulating diabetes, certain breakthroughs have been achieved because of the new capabilities offered by CGM devices and insulin pumps, as two key examples. Even so, it might be that the real potential of new technologies has yet to be realized and that science will yet produce new methods. This research has offered an assessment of glycemia modeling structures to define the role that ICT can play.  
A portion of these recommended platforms has been analyzed, highlighting the lack of overall patient monitoring for an applicable building and contrast of glucose oscillations methods. In this respect, both IoT and IDA have been explored by this research, confirming that these tactics offer the most effective solution for enhancing diabetes management by accurate glycemia estimation, which means that they need to be seen as the ideal approach for handling the extensive data stores required which, in turn, reveals how vital CC is and its likely ongoing impact.  
A monitoring period of 14 days was carried out, including 25 DM1 participants and resulted in the gathering of a number of glycemia-related aspects. In this manner, it needs to be recognized that the obtained dataset incorporates not only CGM measurements from a wide range of individuals over a long timescale and in real-world scenarios, but also includes aspects such as insulin and mealtimes, and other related factors, such as heart rate, sleeping time, and exercise.  
Using such data, and in order to explore the possibilities of glycemia modeling for improved diabetes management, the performance of four intelligent techniques have been tested and properly compared: MLP, SVM, GP, and BRNN. In view of the results obtained, it can be concluded that BRNN is the most powerful and suitable technique to model glucose bloodstream levels, showing the best values of both R-squared (0.83) and RMSE (14.03 mg/dL). Future lines of research will continue to collect data from more patients (also under real-world conditions), and also the authors will explore the capabilities of replicating the present work using deep learning, as a complex approach, and DNM as a very simple methodology, in order to perform a comparison between two different perspectives.  
Conceptualization, I.R.-R., J.-V.R., and M.-A.Z.-I.; methodology, I.R.-R. and J.-V.R.; software, I.R.R.; validation, I.R.-R., J.-V.R., M.-A.Z.-I., J.-M.M.-G.-P., and M.-T.M.-I; formal analysis, I.R.-R., J.-V.R., and M.-A.Z.-I.; investigation, I.R.-R., J.-V.R., and M.-A.Z.-I.; resources, [PERSON]-R., [PERSON]-V.R., M.-A.Z.-I., J.-M.M.-G.-P., and M.-T.M.-I.; data curation, I.R.-R.; writing--original draft preparation, I.R.-R.; writing--review and editing, I.R.-R., [PERSON]-V.R., M.-A.Z.-I., J.-M.M.-G.-P., and M.-T.M.-I.; visualization, [PERSON]-R., [PERSON]-V.R., M.-A.Z.-I., J.-M.M.-G.-P., and M.-T.M.-I.; supervision, [PERSON]-V.R., M.-A.Z.-I., and J.[PERSON]; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Ministerio de Economia y Competitividad (MINECO), Spain (TEC2016-78028-C3-2-P), and by European Fonds Europeen de Developpement Economique et Regional (FEDER) funds.  
[PERSON] would like to thank the support of Programa Operativo FEDER Andalucia 2014-2020 under Project No. UMA18-FEDERJA-023 and Universidad de Malaga, Campus de Excelencia Internacional Andalucia Tech.  
The authors declare no conflict of interest.