Prediction of glucose concentration in type 1 diabetes patients based on machine learning techniques

Type 1 diabetes (T1D) is a type of chronic disease requiring precise glucose management. The use of machine learning (ML) in healthcare offers a promising tool and support to predict glucose concentrations which enhance T1D management in a tremendous manner. This chapter provides an overview of the exploration of ML's role in predicting glucose concentrations in T1D patients. Discussions on the various ML models applied, namely linear regression, support vector machines, random forest models, and deep learning models, with a focus of each with its strengths and limitations. The chapter also elaborates on the challenges in this emerging field, such as the need for high-quality datasets, model interpretability, integration into existing healthcare systems, and patient-specific model adjustments. The potential for improved T1D management and patient outcomes becomes increasingly feasible if we could navigate these hurdles.