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Predicting the Presence of Acute Pulmonary Embolism: A Comparative Analysis of the Artificial Neural Network, Logistic Regression, and Threshold Models

¹ Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Central Radiology Viewing Area, Rm. 117, 600 N. Wolfe St., Baltimore, MD 21287.

OBJECTIVE. The objective of this study was to determine whether an artificial neural network, a new data analysis method, offers increased performance over conventional logistic regression in predicting the presence of a pulmonary embolism for patients in a well-known data set.

MATERIALS AND METHODS. Data from the 1064 patients who received an angiographically based diagnosis of pulmonary embolism in the Prospective Investigation of Pulmonary Embolism Diagnosis study were encoded using a previously described method. The 21 input variables represented abnormalities identified on each patient's ventilation—perfusion scan and chest radiograph. Two methods—an artificial neural network with one hidden layer and a multivariate logistic regression—were compared for accuracy in predicting the presence or absence of pulmonary embolism on subsequent pulmonary arteriography.

RESULTS. No significant difference was observed between the two methods. Areas under the receiver operating characteristic curves ± standard deviation were 0.78 ± 0.02 for the artificial neural network model and 0.79 ± 0.02 for the logistic regression model. Furthermore, use of these two methods resulted in no more diagnostic accuracy than did the use of a simple threshold model based only on the number of subsegmental perfusion defects, which was the dominant input variable.

CONCLUSION. In the study population, the usefulness of data from ventilation—perfusion scans as predictors of the presence of a pulmonary embolism was similar for the three analytic methods, a finding that reinforces the importance of making comparisons to simpler or more established methods when performing studies involving complex analytic models, such as artificial neural networks.

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