AUTHOR=Chan Ka Lung , Leng Xinyi , Zhang Wei , Dong Weinan , Qiu Quanli , Yang Jie , Soo Yannie , Wong Ka Sing , Leung Thomas W. , Liu Jia 

TITLE=Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network

JOURNAL=Frontiers in Neurology

VOLUME=10

YEAR=2019

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2019.00171

DOI=10.3389/fneur.2019.00171

ISSN=1664-2295

ABSTRACT=<p><bold>Background and Purpose:</bold> The risk of recurrent stroke following a transient ischemic attack (TIA) or minor stroke is high, despite of a significant reduction in the past decade. In this study, we investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients.</p><p><bold>Methods:</bold> Consecutive patients with acute TIA or minor ischemic stroke presenting at a tertiary hospital during a 2-year period were recruited. We collected demographics, clinical and imaging data at baseline. The primary outcome was recurrent ischemic stroke within 1 year. We developed ANN models to predict the primary outcome. We randomly down-sampled patients without a primary outcome to 1:1 match with those with a primary outcome to mitigate data imbalance. We used a 5-fold cross-validation approach to train and test the ANN models to avoid overfitting. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on backpropagation to minimize the loss function. We obtained the sensitivity, specificity, accuracy and the c statistic of each ANN model from the 5 rounds of cross-validation and compared that of support vector machine (SVM) and Naïve Bayes classifier in risk stratification of the patients.</p><p><bold>Results:</bold> A total of 451 acute TIA or minor stroke patients were enrolled. Forty (8.9%) patients had a recurrent ischemic stroke within 1 year. Another 40 patients were randomly selected from those with no recurrent stroke, so that data from 80 patients in total were used for 5 rounds of training and testing of ANN models. The median sensitivity, specificity, accuracy and c statistic of the ANN models to predict recurrent stroke at 1 year was 75%, 75%, 75%, and 0.77, respectively. ANN model outperformed SVM and Naïve Bayes classifier in our dataset for predicting relapse after TIA or minor stroke.</p><p><bold>Conclusion:</bold> This pilot study indicated that ANN may yield a novel and effective method in risk stratification of TIA and minor stroke. Further studies are warranted for verification and improvement of the current ANN model.</p>