Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data

BMC Medical Informatics and Decision Making

Research Article

Claes Held¹  Guy Madison²  Mattia Tomasoni³  John Wallert⁴ 

[1] Department of Medical Sciences, Uppsala University, Uppsala, Sweden;Uppsala Clinical Research Center, Uppsala University, Dag Hammarskölds väg 50 A, Uppsala Science Park, 751 83, Uppsala, Sweden;Department of Psychology, Umeå University, Hus Y, Behavioral Sciences Building, Vindarnas Torg, Mediagränd 14 B-115, 901 87, Umeå, Sweden;Department of Public Health and Caring Sciences, Uppsala University, Box 564, Husargatan 3, SE - 75122, Uppsala, Sweden;Department of Public Health and Caring Sciences, Uppsala University, Box 564, Husargatan 3, SE - 75122, Uppsala, Sweden;Department of Women’s and Children’s Health, Uppsala University, Box 572, Husargatan 3, SE - 75123, Uppsala, Sweden;

关键词: Cardiovascular disease;    Classification;    Coronary Artery Syndrome;    Prognostic Modelling;    Myocardial infarction;    Registries;    Supervised machine learning;   

DOI  :  10.1186/s12911-017-0500-y

 received in 2017-01-24, accepted in 2017-06-28,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundMachine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learning algorithms trained on unselected, nation-wide population data from Sweden to solve the binary classification problem of predicting survival versus non-survival 2 years after first myocardial infarction (MI).MethodsThis prospective national registry study for prognostic accuracy validation of predictive models used data from 51,943 complete first MI cases as registered during 6 years (2006–2011) in the national quality register SWEDEHEART/RIKS-HIA (90% coverage of all MIs in Sweden) with follow-up in the Cause of Death register (> 99% coverage). Primary outcome was AUROC (C-statistic) performance of each model on the untouched test set (40% of cases) after model development on the training set (60% of cases) with the full (39) predictor set. Model AUROCs were bootstrapped and compared, correcting the P-values for multiple comparisons with the Bonferroni method. Secondary outcomes were derived when varying sample size (1–100% of total) and predictor sets (39, 10, and 5) for each model. Analyses were repeated on 79,869 completed cases after multivariable imputation of predictors.ResultsA Support Vector Machine with a radial basis kernel developed on 39 predictors had the highest complete cases performance on the test set (AUROC = 0.845, PPV = 0.280, NPV = 0.966) outperforming Boosted C5.0 (0.845 vs. 0.841, P = 0.028) but not significantly higher than Logistic Regression or Random Forest. Models converged to the point of algorithm indifference with increased sample size and predictors. Using the top five predictors also produced good classifiers. Imputed analyses had slightly higher performance.ConclusionsImproved mortality prediction at hospital discharge after first MI is important for identifying high-risk individuals eligible for intensified treatment and care. All models performed accurately and similarly and because of the superior national coverage, the best model can potentially be used to better differentiate new patients, allowing for improved targeting of limited resources. Future research should focus on further model development and investigate possibilities for implementation.

【 授权许可】

CC BY   
© The Author(s). 2017