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.
Key Words
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References
- Short- and long-term prognosis for very old stroke patients. The Copenhagen Stroke Study.Age Ageing. 2004; 33: 149-154
- Predicting functional outcome and survival after acute ischemic stroke.J Neurol. 2002; 249: 888-895
- Validation and comparison of models predicting survival following intracerebral hemorrhage.Crit Care Med. 1995; 23: 950-954
- Artificial neural networks improve the prediction of mortality in intracerebral hemorrhage.Neurology. 1999; 53: 351-357
- Predicting outcomes of patients in Japan after first acute stroke using a simple model.Am J Phys Med Rehabil. 2001; 80: 645-649
- A predictive risk model for outcomes of ischemic stroke.Stroke. 2000; 31: 448-455
- Predicting outcome after acute and subacute stroke: development and validation of new prognostic models.Stroke. 2002; 33: 1041-1047
- Performance of a statistical model to predict stroke outcome in the context of a large, simple, randomized, controlled trial of feeding.Stroke. 2003; 34: 127-133
- Predicting outcome after acute ischemic stroke: an external validation of prognostic models.Neurology. 2004; 62: 581-585
- Predicting outcome in hyper-acute stroke: validation of a prognostic model in the Third International Stroke Trial (IST3).J Neurol Neurosurg Psychiatry. 2008; 79 (Epub 2007 Aug 31): 397-400
- Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials.Stroke. 2008; 39: 1821-1826
- IScore: a risk score to predict death early after hospitalization for an acute ischemic stroke.Circulation. 2011; 123: 739-749
- Application and performance of two stroke outcome prediction models in a Chinese population.PM R. 2012; 4: 123-128
- Five-year survival after first-ever stroke and related prognostic factors in the Perth Community Stroke Study.Stroke. 2000; 31: 2080-2086
- Reliability of the variables in a new set of models that predict outcome after stroke.J Neurol Neurosurg Psychiatry. 2003; 74: 447-451
- What determines good recovery in patients with the most severe strokes? The Copenhagen Stroke Study.Stroke. 1999; 30: 2008-2012
- Learning representation by back-propagating errors.Nature. 1986; 323: 533-536
- Neural networks in computer intelligence.McGraw-Hill, USA1994: 31
- Multilayer networks. Neural network fundamentals with graphs, algorithms and applications.McGraw-Hill, USA1996: 155-212
- Yapay sinir ağı modeli çok katmanlıalgılayıcı. Yapay Sinir Ağları.Papatya Yayıncılık, İstanbul, Türkiye2003: 75-114
- Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: a comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey.Expert Syst Appl. 2009; 36: 3355-3366
Article info
Publication history
Published online: March 27, 2014
Accepted:
December 16,
2013
Received in revised form:
December 5,
2013
Received:
November 12,
2013
Identification
DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2013.12.018
Copyright
© 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.
