A machine learning approach for the prediction of pulmonary hypertension.
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 sever...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0224453 |
| _version_ | 1818916456378662912 |
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| author | Andreas Leha Kristian Hellenkamp Bernhard Unsöld Sitali Mushemi-Blake Ajay M Shah Gerd Hasenfuß Tim Seidler |
| author_facet | Andreas Leha Kristian Hellenkamp Bernhard Unsöld Sitali Mushemi-Blake Ajay M Shah Gerd Hasenfuß Tim Seidler |
| author_sort | Andreas Leha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T00:18:28Z |
| format | Article |
| id | doaj.art-eb2f5685e5a94f40a316feb42220fbac |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T00:18:28Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-eb2f5685e5a94f40a316feb42220fbac2022-12-21T20:00:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-011410e022445310.1371/journal.pone.0224453A machine learning approach for the prediction of pulmonary hypertension.Andreas LehaKristian HellenkampBernhard UnsöldSitali Mushemi-BlakeAjay M ShahGerd HasenfußTim SeidlerBACKGROUND: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.https://doi.org/10.1371/journal.pone.0224453 |
| spellingShingle | Andreas Leha Kristian Hellenkamp Bernhard Unsöld Sitali Mushemi-Blake Ajay M Shah Gerd Hasenfuß Tim Seidler A machine learning approach for the prediction of pulmonary hypertension. PLoS ONE |
| title | A machine learning approach for the prediction of pulmonary hypertension. |
| title_full | A machine learning approach for the prediction of pulmonary hypertension. |
| title_fullStr | A machine learning approach for the prediction of pulmonary hypertension. |
| title_full_unstemmed | A machine learning approach for the prediction of pulmonary hypertension. |
| title_short | A machine learning approach for the prediction of pulmonary hypertension. |
| title_sort | machine learning approach for the prediction of pulmonary hypertension |
| url | https://doi.org/10.1371/journal.pone.0224453 |
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