Short-term Glucose Prediction based on Oral Glucose Tolerance Test Values

Abnormal glucose metabolism increases the risk for cardiovascular disease and mortality. A key motivation for investigating this topic is Diabetes prevalence, which is the most common example of metabolic disorder that concern humans all over the world. The oral glucose tolerance test (OGTT) constitutes a traditional medical screening tool for all types of diabetes such as prediabetes, gestational, type 2 diabetes, insulin resistance or discrimination of Impaired Glucose Tolerance (IGT) from Natural Glucose Tolerance (NGT) individuals. Another motivation for this study is that a plethora of studies has shown the effectiveness of machine learning in glycemic control and improvement of diabetic's management. This research study aims to evaluate the adequacy of machine learning on the short-term prediction of glucose levels. The main contribution of this analysis is a Random Forest regression tree model which, has been trained considering various risk factors and glucose samples obtained by a 2-hour OGTT, after a fast and then after an oral intake of glucose, at intervals of 30 minutes. The research outcomes verify the efficacy of Random Forest (RF).