In the 21st century, every year, approximately 880 thousand people living in Europe suffer an ischemic stroke. Predicting the patient’s outcome is key to choosing the course of treatment. In this master thesis, it was predicted the functional outcome, by the binary version, of the modified Rankin Scale at two points in time: three months and one year after the stroke took place. Often, data provided by health organisations to conduct these studies is incomplete which can impair the results. Thus the need arises to choose a proper way to handle the missing data. Here missing values were imputed with six different methods and the classifiers were then trained with seven distinct machine learning models. It was shown the area under the receiver operating characteristic curve for the best classifiers, at the three months and one-year marks, are 0.8217 and 0.7537, respectively. Moreover, it was not found a statistically significant difference between the performance of the distinct imputation methods for each machine learning model.