Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

Abstract

Background Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to compare the accuracies of GRNN and LR models to identify the most optimal model for the prediction of acute stroke outcome, as well as explore useful biomarkers for predicting the prognosis of acute stroke patients. Method In a single-center study, 216 (80% for the training set and 20% for the test set) acute stroke patients admitted to the Shenzhen Second People’s Hospital between December 2019 to June 2021 were retrospectively recruited. The functional outcomes of the patients were measured using Barthel Index (BI) on discharge. A training set was used to optimize the GRNN and LR models. The test set was utilized to validate and compare the performances of GRNN and LR in predicting acute stroke outcome based on the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and the Kappa value. Result The LR analysis showed that age, the National Institute Health Stroke Scale score, BI index, hemoglobin, and albumin were independently associated with stroke outcome. After validating in test set using these variables, we found that the GRNN model showed a better performance based on AUROC (0.931 vs 0.702), sensitivity (0.933 vs 0.700), specificity (0.889 vs 0.722), accuracy (0.896 vs 0.729), and the Kappa value (0.775 vs 0.416) than the LR model. Conclusion Overall, the GRNN model demonstrated superior performance to the LR model in predicting the prognosis of acute stroke patients. In addition to its advantage in not affected by implicit interactions and complex relationship in the data. Thus, we suggested that GRNN could be served as the optimal statistical model for acute stroke outcome prediction. Simultaneously, prospective validation based on more variables of the GRNN model for the prediction is required in future studies.

Clinical phenotypes of patients with acute stroke: a secondary analysis

“…Qu et al studied 216 patients with acute stroke who were admitted to the Shenzhen Second People's Hospital between December 2019 to June 2021 [5]. They compared the accuracies of generalized regression neural networks and logistic regression models to identify an optimal model for predicting acute stroke outcomes.…”

“…The outcome variable was the Barthel Index (BI) on discharge. It was reported that the generalized regression neural network showed a better performance than the logistic regression [5]. The primary study was approved by the Ethics Committee of Shenzhen Second People's Hospital.…”

“…Functional recovery is suggested to be monitored in patients with stroke [17]. In Qu's study, the BI was used for evaluating functional recovery [5]. One professional physiotherapist measured the BI at baseline and on discharge [5].…”

“…In Qu's study, the BI was used for evaluating functional recovery [5]. One professional physiotherapist measured the BI at baseline and on discharge [5]. The BI was initially developed to assess daily activities in patients with neuromuscular disorders [18].…”

“…Commonly, a set of demographic and clinical risk factors has been investigated to risk-stratify patients using regression and classification techniques [5,6]. Researchers tried to increase the predictive ability of clinical models by applying statistical methods and machine learning algorithms [4][5][6].…”