ArticleProceedings. AMIA Symposium2000
Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression.
Article in Proceedings. AMIA Symposium, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- "Stay at Home": The Effects of the COVID-19 Lockdown on Household Food Waste in Colombia.Frontiers in psychology · 2021Article
- Cervical cancer risk and access: Utilizing three statistical tools to assess Haitian women in South Florida.PloS one · 2021Article
- Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study.Journal of the American Academy of Dermatology · 2020Observational
- Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality.International journal of environmental research and public health · 2020Article
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- Use of a multilayer perceptron to create a prediction model for dressing independence in a small sample at a single facility.Journal of physical therapy science · 2019Article
- The role of physical activity in the development of first cardiovascular disease event: a tree-structured survival analysis of the Danish ADDITION-PRO cohort.Cardiovascular diabetology · 2018Observational
- Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes.Health services research · 2018Article
- Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.Circulation research · 2017Observational
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- A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.Statistics in medicine · 2015Article
- Incidence of and risk factors for sudden cardiac death in children with dilated cardiomyopathy: a report from the Pediatric Cardiomyopathy Registry.Journal of the American College of Cardiology · 2012Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
11079864PMC2244093What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.