Electronic Health Records (EHRs) contain valuable historical information for building clinical decision support systems. In this study, we focus on exploring novel techniques for improving the prediction of the severity degree of Diabetic Retinopathy (DR) in Diabetes Mellitus patients. In a previous paper, we evaluated the behaviour of different classifiers using the patients’ retrospective EHR data to assess their current level of DR, achieving good results. Continuing that work, we now focus on studying different methods for encoding numerical variables, in order to improve the accuracy of these predictions. We propose three normalization methods based on fuzzy sets for encoding numerical data. Because of the inherent uncertainty of medical data, using fuzzy logic to represent the numerical variables can enhance the accuracy of a classifier. The results of the experimental tests, conducted on a dataset of 2108 patients, show that for low-complexity classifiers (such as KNN or CNN) a classical fuzzification technique works the best, while for more complex architectures (like TapNet or ResNet) a fuzzy two-hot encoding gives the best performance. The final aim of the research is to build a clinical decision support system that can make an accurate and personalised prediction of DR evolution.