Predicting progression from a stage of Mild Cognitive Impairment to Alzheimer’s disease is a major pursuit in current dementia research. As a result, many prognostic models have emerged with the goal of supporting clinical decisions. Despite the efforts, the clinical application of such models has been hampered by: 1) the lack of a reliable assessment of the uncertainty of each prediction, and 2) not knowing the time to conversion. It is paramount for clinicians to know how much they can rely on the prediction made for a given patient (conversion or no conversion), and the time windows in case of conversion, in order to timely adjust the treatments. We propose a supervised learning approach using Conformal Prediction and a stepwise learning approach, where the learning model first predicts whether a patient converts to dementia, or remains stable, and then predicts the more likely progression window (short-term or long-term conversion). We used data from ADNI to test the approach and predict conversion within time windows of up to 2 years (short-term converter) and 2 to 4 years (long-term converter). The exploratory results are promising but compromised by the small number of examples for the long-term converting patients, available for training.