Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.
Artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression, the most commonly used method for developing predictive models for dichotomous outcomes in medicine.
Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms.
Disadvantages include its « black box » nature, greater computational burden, proneness to overfitting, and the empirical nature of model development.
An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.
Mots-clés Pascal : Intelligence artificielle, Algorithme, Régression logistique, Analyse statistique, Modèle mathématique, Pronostic, Prédiction, Méthodologie, Evaluation, Homme, Epidémiologie, Etude comparative, Etats Unis, Amérique du Nord, Amérique
Mots-clés Pascal anglais : Artificial intelligence, Algorithm, Logistic regression, Statistical analysis, Mathematical model, Prognosis, Prediction, Methodology, Evaluation, Human, Epidemiology, Comparative study, United States, North America, America
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 97-0056631
Code Inist : 002B30A01A1. Création : 21/05/1997.