Prediction of mortality in stroke patients using multilayer perceptron neural networks
Küçük Resim Yok
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Tubitak Scientific & Technological Research Council Turkey
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Aim: We aimed to predict mortality in stroke patients by using multilayer perceptron (MLP) neural networks. Materials and methods: A data set consisting of 584 stroke patients was analyzed using MLP neural networks. The effect of prognostic factors (age, hospitalization time, sex, hypertension, atrial fibrillation, embolism, stroke type, infection, diabetes mellitus, and ischemic heart disease) on mortality in stroke were trained with 6 different MLP algorithms [quick propagation (QP), Levenberg-Marquardt (LM), backpropagation (BP), quasi-Newton (QN), delta bar delta (DBD), and conjugate gradient descent (CGD)]. The performances of the MLP neural network algorithms were compared using the receiver operating characteristic (ROC) curve method. Results: Among the 6 algorithms that were trained with the MLP, QP achieved the highest specificity (81.3%), sensitivity (78.4%), accuracy (80.7%), and area under the curve (AUC) (0.869) values, while CGD achieved the lowest specificity (61.5%), sensitivity (58.7%), accuracy (60.8%), and AUC (0.636) values. The AUC of the QP algorithm was statistically significantly higher than the AUCs of the QN, DBD, and CGD algorithms (P < 0.05 for all of the pairwise comparisons). Conclusion: The MLP trained with the QP algorithm achieved the highest specificity, sensitivity, accuracy, and AUC values. This can be helpful in the prediction of mortality in stroke.
Açıklama
Anahtar Kelimeler
Multilayer Perceptron Neural Networks, Stroke, Mortality, Algorithm, Disease, Classification, Diagnosis, Signals, System
Kaynak
Turkish Journal Of Medical Sciences
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
42
Sayı
5
