Advances in biological time series prediction by neural networks

In this paper, we consider some different aspects involved in the prediction of biological time series. This is often a difficult task, which can be accomplished by defining a suited function approximation problem whose inputs are determined using past samples of the sequence to be predicted. We demonstrate that neural networks can be very useful in this regard. For instance, we illustrate how Mixture of Gaussian neural networks can be efficiently used for the prediction of glucose temporal evolution in Diabetes Mellitus Type 2, instead of using the well-known MINMOD model. We also focus on the chaotic nature of biological time series, which can be contaminated by spurious noise. Neural networks represent a powerful tool for the prediction of these sequences, when the past samples to be used for prediction are chosen by suitable embedding techniques. A new embedding approach is proposed in the paper, using a genetic algorithm where each individual represents a possible embedding solution. We demonstrate that the proposed technique yields better prediction accuracies, considering as a particular application the prediction of those biological parameters (glucose, heart rate, and skin conductivity) that are strictly related to road safety, since they may evidence stress conditions or possible loss of consciousness.