A machine learning approach for the prediction of pulmonary hypertension

• Published in: PloS one Vol. 14; no. 10; p. e0224453
• Main Authors: Leha, Andreas, Hellenkamp, Kristian, Unsöld, Bernhard, Mushemi-Blake, Sitali, Shah, Ajay M., Hasenfuß, Gerd, Seidler, Tim
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.10.2019; Public Library of Science (PLoS)
• Subjects: Aged; Algorithms; Analysis; Artificial intelligence; Cardiology; Care and treatment; Classification; Computer and Information Sciences; Electrocardiography; Engineering and Technology; Female; Forests; Heart failure; Humans; Hypertension; Hypertension, Pulmonary - diagnosis; Internal medicine; Learning algorithms; Logistic regression; Machine Learning; Male; Medicine; Medicine and Health Sciences; Middle Aged; Physical Sciences; Predictions; Prognosis; Pulmonary arteries; Pulmonary artery; Pulmonary hypertension; Regression analysis; Research and Analysis Methods; Risk Assessment; Risk Factors; ROC Curve; Support vector machines; Values; Variables; Veins & arteries; United Kingdom > UK; England; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0224453

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. 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. 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. 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.