Diabetic patients usually take insulin bolus right before eat-ing a meal. A wrong dosage of insulin may lead to a hypoglycemia. Be-ing able to anticipate such insulin-induced, postprandial hypoglycemias would enable warning of the patients about the risk associated with the quantity of insulin they are planning to take. In this work, we explore the feasibility of predicting these postprandial hypoglycemias by using information available at pre-meal time, such as glucose levels, planned insulin intakes and carbohydrates estimations. First, an experiment has been done on a dataset acquired on real patients, for which several classes of machine learning algorithms have been tried. The obtained results do not offer predictions that are useful enough to consider any usage in real-life applications. These kinds of datasets — acquired on real patients — suffer heavily from missing data and incorrect carbohydrates estimations though. In order to analyse the impact of these flaws on the obtained results, the same experiment has been run on a simulated dataset. Re-sults support that even with the simulated dataset, which does not have missing data and which has precise carbohydrates intake, these features alone are not able to predict postprandial hypoglycemia. Therefore, im-proving the quality of patients annotations is not enough to solve the problem, and using these features without further features engineering does not offer good results.