Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression.
Proc AMIA Symp
; : 156-60, 2000.
Article
em En
| MEDLINE
| ID: mdl-11079864
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Árvores de Decisões
/
Doenças Cardiovasculares
/
Modelos Logísticos
/
Redes Neurais de Computação
/
Medição de Risco
Tipo de estudo:
Etiology_studies
/
Evaluation_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Proc AMIA Symp
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2000
Tipo de documento:
Article
País de afiliação:
França