Abstract: Various examples are provided related to the application of a kernel neural network (KNN) to the analysis of high dimensional and ultrahigh dimensional data for, e.g., risk prediction. In one embodiment, a method includes training a KNN with a training set to produce a trained KNN model, determining a likelihood of a condition based at least in part upon an output indication of the trained KNN corresponding to one or more phenotypes, identifying treatment or prevention strategy for an individual based at least in part upon the likelihood of the condition. The KNN model includes a plurality of kernels as a plurality of layers to capture complexity between the data with disease phenotypes. The training set of data includes genetic information applied as inputs to the KNN and the phenotype(s), and the output indication is based upon analysis of data comprising genetic information from the individual by the trained KNN.