A Reliable Machine Learning Approach applied to Single-Cell Classification in Acute Myeloid Leukemia

Machine Learning research applied to the medical field is increasing. However, few of the proposed approaches are actually deployed in clinical settings. One reason is that current methods may not be able to generalize on new unseen instances which differ from the training population, thus providing unreliable classifications. Approaches to measure classification reliability could be useful to assess whether to trust prediction on new cases. Here, we propose a new reliability measure based on the similarity of a new instance to the training set. In particular, we evaluate whether this example would be selected as informative by an instance selection method, in comparison with the available training set. We show that this method distinguishes reliable examples, for which we can trust the classifier's prediction, from unreliable ones, both on simulated data and in a real-case scenario, to distinguish tumor and normal cells in Acute Myeloid Leukemia patients.