From Risk Prediction to Risk Factors Interpretation. Comparison of Neural Networks and Classical Statistics for Dementia Prediction

It is proposed to investigate the onset of a disease D, based on several risk factors., with a specific interest in Alzheimer occurrence. For that purpose, two classes of techniques are available, whose properties are quite different in terms of interpretation, which is the focus of this paper: classical statistics based on probabilistic models and artificial intelligence (mainly neural networks) based on optimization algorithms. Both methods are good at prediction, with a preference for neural networks when the dimension of the potential predictors is high. But the advantage of the classical statistics is cognitive : the role of each factor is generally summarized in the value of a coefficient which is highly positive for a harmful factor, close to 0 for an irrelevant one, and highly negative for a beneficial one.