On the Management of Type 1 Diabetes Mellitus with IoT Devices and ML Techniques
This paper addresses the challenge of managing Type 1 Diabetes Mellitus for patients by proposing an IoT and ML-based system, representing an incremental step in applying existing technologies to a specific health domain.
This paper outlines a research direction for managing Type 1 Diabetes Mellitus by integrating IoT devices for data collection via biosensors and applying machine learning techniques to predict blood glucose values. The project aims to explore data processing in the cloud, biodevice interconnection, and the trade-offs between local and cloud computing.
The purpose of this Conference is to present the main lines of base projects that are founded on research already begun in previous years. In this sense, this manuscript will present the main lines of research in Diabetes Mellitus type 1 and Machine Learning techniques in an Internet of Things environment, so that we can summarize the future lines to be developed as follows: data collection through biosensors, massive data processing in the cloud, interconnection of biodevices, local computing vs. cloud computing, and possibilities of machine learning techniques to predict blood glucose values, including both variable selection algorithms and predictive techniques.