Classification of preclinical markers in Alzheimer's disease via WiSARD classifier.

Weightless Neural Networks (WNN) showed good results in various classification problems in different domains where a significant number of instances for each class was available. In this work, we present different WiSARD classifiers facing a quite difficult problem from both the clinical and the machine learning point of views: the classification of preclinical markers in Alzheimer's disease continuum patients. The four domain classes show overlapping molecular features and each has few instances (around 40). Together with improved class separation, the confirmation of the goodness of the results is given by a series of experiments that have compared the WiSARD classifiers to many state-of-the-art classifiers, even those ensembles, showing that the obtained results are very close to the top best models.