Abstract
Hemorrhagic transformation (HT) is one of the leading causes of a poor prognostic marker after acute ischemic stroke (AIS). We compared the performances of the several machine learning (ML) algorithms to predict HT after AIS using only structured data. A total of 2028 patients with AIS, who were admitted within seven days of symptoms onset, were included in this analysis. HT was defined based on the criteria of the European Co-operative Acute Stroke Study-II trial. The whole dataset was randomly divided into a training and a test dataset with a 7:3 ratio. Binary logistic regression, support vector machine, extreme gradient boosting, and artificial neural network (ANN) algorithms were used to assess the performance of predicting the HT occurrence after AIS. Five-fold cross validation and a grid search technique were used to optimize the hyperparameters of each ML model, which had its performance measured by the area under the receiver operating characteristic (AUROC) curve. Among the included AIS patients, the mean age and number of male subjects were 69.6 years and 1183 (58.3%), respectively. HT was observed in 318 subjects (15.7%). There were no significant differences in corresponding variables between the training and test dataset. Among all the ML algorithms, the ANN algorithm showed the best performance in terms of predicting the occurrence of HT in our dataset (0.844). Feature scaling including standardization and normalization, and the resampling strategy showed no additional improvement of the ANN’s performance. The ANN-based prediction of HT after AIS showed better performance than the conventional ML algorithms. Deep learning may be used to predict important outcomes for structured data-based prediction.
Highlights
In previous studies, the performance of predicting Hemorrhagic transformation (HT) via C-statistics showed relatively poor predictive power at Recently, machine learning (ML) or deep learning (DL) algorithms have been widely used in medical practice as a clinical decision support system [9,10]
The usefulness of the ML strategy to predict the risk of HT following acute ischemic stroke (AIS) was assessed [11,12,13,14,15]
Wang et al reported that the neural network model showed the best performance (AUROC = 0.82) to predict symptomatic intracerebral hemorrhage (ICH) following thrombolysis in patients with AIS [11]
Summary
The usefulness of the ML strategy to predict the risk of HT following AIS was assessed [11,12,13,14,15]. Wang et al reported that the neural network model showed the best performance (AUROC = 0.82) to predict symptomatic intracerebral hemorrhage (ICH) following thrombolysis in patients with AIS [11]. In another multicenter trial using the Observational Medical Outcomes Partnership Common Data model, the least absolute shrinkage and selection operator regression model showed an AUROC of 0.75 to predict
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