Random Forest-Based Prediction of Acute Respiratory Distress Syndrome in
Patients Undergoing Cardiac Surgery
Abstract
Objective: To develop a machine learning-based model for predicting the
risk of acute respiratory distress syndrome (ARDS) after cardiac
surgery. Methods: Data were collected from 1011 patients who underwent
cardiac surgery between February 2018 and September 2019. We developed a
predictive model on ARDS by using the random forest algorithm of machine
learning. The discrimination of the model was then shown by the area
under the curve (AUC) of the receiver operating characteristic curve.
Internal validation was performed by using a 5-fold cross-validation
technique, so as to evaluate and optimize the predictive model. Model
visualization was performed to reveal the most influential features
during the model output. Results: Of the 1011 patients included in the
study, 53 (5.24%) suffered ARDS episodes during the first postoperative
week. This random forest distinguished ARDS patients from non-ARDS
patients with an AUC of 0.932 (95% CI=0.896-0.968) in the training set
and 0.864 (95% CI=0.718-0.997) In the final test set. The top 10
variables in the random forest were cardiopulmonary bypass time,
transfusion red blood cell, age, EUROSCORE II Score, albumin,
hemoglobin, operation time, serum creatinine, diabetes, and type of
surgery. Conclusion: Our findings suggest that machine learning
algorithm is highly effective in predicting ARDS in patients undergoing
cardiac surgery. The successful application of the generated random
forest may guide clinical decision making and aid in improving the
long-term prognosis of patients.