Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network
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AbstractBackground and Purpose: 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.Methods: 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 overfilling. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on back propagation 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 Naive Bayes classifier in risk stratification of the patients.Results: 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 Naive Bayes classifier in our dataset for predicting relapse after TIA or minor stroke.Conclusion: 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.
All Author(s) ListChan KL, Leng XY, Zhang W, Dong WN, Qiu QL, Yang J, Soo Y, Wong KS, Leung TW, Liu J
Journal nameFrontiers in Neurology
Volume Number10
Article number171
LanguagesEnglish-United Kingdom
Keywordstransient ischemic attack, minor stroke, artificial neural network, risk stratification, prognosis
Web of Science Subject CategoriesClinical Neurology;Neurosciences;Neurosciences & Neurology

Last updated on 2021-21-01 at 02:05