title: Artificial neural networks for small dataset analysis.
abstract: Artificial neural networks (ANNs) are usually considered as tools which can help to analyze cause-effect relationships in complex systems within a big-data framework. On the other hand, health sciences undergo complexity more than any other scientific discipline, and in this field large datasets are seldom available. In this situation, I show how a particular neural network tool, which is able to handle small datasets of experimental or observational data, can help in identifying the main causal factors leading to changes in some variable which summarizes the behaviour of a complex system, for instance the onset of a disease. A detailed description of the neural network tool is given and its application to a specific case study is shown. Recommendations for a correct use of this tool are also supplied.
authors.affiliation: CNR, Institute of Atmospheric Pollution Research, Rome, Italy.
authors.forename: Antonello
authors.initials: A
authors.lastname: Pasini
keywords: Neural networks; causal influences; complex systems; nonlinear regression; small datasets