AI Summary & Key Takeaways
AI InsightThis can be helpful in the prediction of mortality in stroke.
📌 3 Key Takeaways
Abstract
We aim ed 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.
Özet
We aim ed 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.