  Most machine learning classifiers give predictions for new examples
accurately, yet without indicating how trustworthy predictions are. In the
medical domain, this hampers their integration in decision support systems,
which could be useful in the clinical practice. We use a supervised learning
approach that combines Ensemble learning with Conformal Predictors to predict
conversion from Mild Cognitive Impairment to Alzheimer's Disease. Our goal is
to enhance the classification performance (Ensemble learning) and complement
each prediction with a measure of credibility (Conformal Predictors). Our
results showed the superiority of the proposed approach over a similar ensemble
framework with standard classifiers.
