Publication: Prediction of intracerebral hemorrhage following thrombolytic therapy for acute ischemic stroke using multiple artificial neural networks
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Issued Date
2012-03-01
Resource Type
ISSN
17431328
01616412
01616412
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2-s2.0-84857943340
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Mahidol University
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SCOPUS
Bibliographic Citation
Neurological Research. Vol.34, No.2 (2012), 120-128
Suggested Citation
Permphan Dharmasaroja, Pornpatr A. Dharmasaroja Prediction of intracerebral hemorrhage following thrombolytic therapy for acute ischemic stroke using multiple artificial neural networks. Neurological Research. Vol.34, No.2 (2012), 120-128. doi:10.1179/1743132811Y.0000000067 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/14933
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Title
Prediction of intracerebral hemorrhage following thrombolytic therapy for acute ischemic stroke using multiple artificial neural networks
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Abstract
Objectives: Artificial neural networks (ANNs) have been increasingly used in diagnosis and the prediction of outcome, mortality, and risk factors in ischemic stroke. Each model may have different accuracy, sensitivity, and specificity in processing the same clinical information. Thus, using only one model of ANNs may mislead the prediction. The present study aimed to predict symptomatic intracerebral hemorrhage (SICH) following thrombolysis in acute ischemic stroke based on clinical, laboratory, and imaging data using multiple ANN models. Methods: Models for radial basis function (RBF), multilayer perceptron (MLP), probabilistic neural network (PNN), and support vector machine (SVM) were generated to analyze 194 datasets with 29 predictive variables. The relative importance of each predictor variable was calculated using sensitivity analysis. Results: Comparison among the models based on the areas under the receiver operating characteristic curves (AUC) showed no significantly statistical difference in predictive performance among RBF, MLP, and PNN. PNN showed significantly better performance than SVM. With a minimum importance score of 50 together with an AUC value > 0.50, three models identified stroke subtype as an important predictive variable for SICH. Other potential predictors were stroke location, prothrombin time, low-density-lipoprotein cholesterol, diastolic blood pressure, International Normalized Ratio, and brain computed tomography findings. Discussion: Although ANN models showed similar performance, the classification results were not totally alike, suggesting an advantage of using multiple classification models over a single model. The predictive results are supported by previous statistical studies on different datasets, suggesting generalizability of the utility of ANN analyses. © W. S. Maney & Son Ltd 2012.