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It is proposed to investigate the onset of a disease D, based on several riskfactors., with a specific interest in Alzheimer occurrence. For that purpose,two classes of techniques are available, whose properties are quite differentin terms of interpretation, which is the focus of this paper: classicalstatistics based on probabilistic models and artificial intelligence (mainlyneural networks) based on optimization algorithms. Both methods are good atprediction, with a preference for neural networks when the dimension of thepotential predictors is high. But the advantage of the classical statistics iscognitive : the role of each factor is generally summarized in the value of acoefficient which is highly positive for a harmful factor, close to 0 for anirrelevant one, and highly negative for a beneficial one.

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