Prediction of intracerebral hemorrhage following thrombolytic therapy for acute ischemic stroke using multiple artificial neural networks

Neurological Research
Permphan Dharmasaroja, Pornpatr A Dharmasaroja

Abstract

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. 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. 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. ...Continue Reading

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Jun 18, 2014·NeuroImage. Clinical·Paul BentleyDaniel Rueckert

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