Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression.
The estimate of a multivariate risk is now required in guidelines for cardiovascular prevention. Limitations of existing statistical risk models lead to explore machine-learning methods. This study evaluates the implementation and performance of a decision tree (CART) and a multilayer perceptron (MLP) to predict cardiovascular risk from real data. The study population was randomly splitted in a learning set (n = 10,296) and a test set (n = 5,148). CART and the MLP were implemented at their best performance on the learning set and applied on the test set and compared to a logistic model. Implementation, explicative and discriminative performance criteria are considered, based on ROC analysis. Areas under ROC curves and their 95% confidence interval are 0.78 (0.75-0.81), 0.78 (0.75-0.80) and 0.76 (0.73-0.79) respectively for logistic regression, MLP and CART. Given their implementation and explicative characteristics, these methods can complement existing statistical models and contribute to the interpretation of risk.
Colombet
I
I
Medical Informatics Department, Broussais Hospital, Paris, France. colombet@hbroussais.fr
Ruelland
A
A
Chatellier
G
G
Gueyffier
F
F
Degoulet
P
P
Jaulent
M C
MC
D001185:Artificial Intelligence; D002318:Cardiovascular Diseases / Q000517:prevention & control; D016208:Databases, Factual; D003663:Decision Trees; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors