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Cerebrovascular accident (CVA) or stroke is the rapid loss of brain functiondue to disturbance in the blood supply to the brain. Statistically, stroke isthe second leading cause of death. This has motivated us to suggest a two-tiersystem for predicting stroke; the first tier makes use of Artificial NeuralNetwork (ANN) to predict the chances of a person suffering from stroke. The ANNis trained the using the values of various risk factors of stroke of severalpatients who had stroke. Once a person is classified as having a high risk ofstroke, s/he undergoes another the tier-2 classification test where his/herneuro MRI (Magnetic resonance imaging) is analysed to predict the chances ofstroke. The tier-2 uses Non-negative Matrix Factorization and Haralick Texturalfeatures for feature extraction and SVM classifier for classification. We haveobtained an accuracy of 96.67% in tier-1 and an accuracy of 70% in tier-2.

Two Tier Prediction of Stroke Using Artificial Neural Networks and Support Vector Machines