Predicting 10-day Mortality in Patients with Strokes Using Neural Networks and Multivariate Statistical Methods

Background The aim of the present study was to evaluate the performance of 2 different multivariate statistical methods and artificial neural networks (ANNs) in predicting the mortality of hemorrhagic and ischemic patients within the first 10 days after stroke. Methods The multilayer perceptron (MLP) ANN model and multivariate statistical methods (multivariate discriminant analysis [MDA] and logistic regression analysis [LRA]) have been used to predict acute stroke mortality. The data of total 570 patients (230 hemorrhagic and 340 ischemic stroke), who were admitted to the hospital within the first 24 hours after stroke onset, have been used to develop prediction models. The factors affecting the prognosis were used as inputs for prediction models. Survival or death status of the patients was taken as output of the models. Results For the MLP method, the accuracies were 99.9% in a training data set and 80.9% in a testing data set for the hemorrhagic group, whereas 97.8% and 75.9% for the ischemic group, respectively. For the MDA method, the training and testing performances were 89.8%, 87.8% and 80.6%, 79.7% for hemorrhagic and ischemic groups, respectively. For the LRA method, the training and testing performances for the hemorrhagic group were 89.7% and 86.1%, and for the ischemic group were 81.7% and 80.9%, respectively. Conclusions Training and test performances yielded different results for ischemic and hemorrhagic groups. MLP method was most successful for the training phase, whereas LRA and MDA methods were successful for the test phase. In the hemorrhagic group, higher prediction performances were achieved for both training and testing phases.