A machine learning approach for the prediction of pulmonary hypertension


Autoři: Andreas Leha aff001;  Kristian Hellenkamp aff002;  Bernhard Unsöld aff003;  Sitali Mushemi-Blake aff004;  Ajay M. Shah aff004;  Gerd Hasenfuß aff002;  Tim Seidler aff002
Působiště autorů: Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany aff001;  Clinic for Cardiology and Pulmonology/Heart Center, University Medical Center Göttingen, Göttingen, Germany aff002;  Department of Internal Medicine II, University of Regensburg, Regensburg, Germany aff003;  King’s College London British Heart Foundation Centre, School of Cardiovascular Medicine & Sciences, London, England, United Kingdom aff004;  DZHK (German Centre for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224453

Souhrn

Background

Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters.

Methods

In our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PAP obtained within 24 hours, we trained and applied five ML algorithms (random forest of classification trees, random forest of regression trees, lasso penalized logistic regression, boosted classification trees, support vector machines) using a 10 times 3-fold cross-validation (CV) scheme.

Results

ML algorithms achieved high prediction accuracies: support vector machines (AUC 0.83; 95% CI 0.73–0.93), boosted classification trees (AUC 0.80; 95% CI 0.68–0.92), lasso penalized logistic regression (AUC 0.78; 95% CI 0.67–0.89), random forest of classification trees (AUC 0.85; 95% CI 0.75–0.95), random forest of regression trees (AUC 0.87; 95% CI 0.78–0.96). In contrast to the best of several conventional formulae (by Aduen et al.), this ML algorithm is based on several echocardiographic signs and feature selection, with estimated right atrial pressure (RAP) being of minor importance.

Conclusions

Using ML, we were able to predict pulmonary hypertension based on a broader set of echocardiographic data with little reliance on estimated RAP compared to an existing formula with non-inferior performance. With the conceptual advantages of a broader and unbiased selection and weighting of data our ML approach is suited for high level assistance in PH prediction.

Klíčová slova:

Algorithms – Boosting algorithms – Decision trees – Echocardiography – Forecasting – Machine learning – Machine learning algorithms – Pulmonary hypertension


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2019 Číslo 10