论文标题
使用人工神经网络和支持向量机的两层预测中风的预测
Two Tier Prediction of Stroke Using Artificial Neural Networks and Support Vector Machines
论文作者
论文摘要
脑血管事故（CVA）或中风是由于对大脑的血液供应的干扰，大脑功能的迅速丧失。从统计上讲，中风是死亡的第二大原因。这促使我们提出了一个两层预测中风的系统。第一层利用人工神经网络（ANN）来预测患有中风的人的机会。 ANN使用中风的几名患者的中风的各种风险因素的值对ANN进行了训练。一旦一个人被归类为具有中风的高风险，他/她将经历另一项分类测试，其中分析了他/她的神经MRI（磁共振成像）以预测中风的机会。 Tier-2使用非负矩阵分解和Haralick纹理特征进行特征提取和SVM分类器进行分类。我们在Tier-1中获得了96.67％的准确性，在TIER-2中的准确度为70％。
Cerebrovascular accident (CVA) or stroke is the rapid loss of brain function due to disturbance in the blood supply to the brain. Statistically, stroke is the second leading cause of death. This has motivated us to suggest a two-tier system for predicting stroke; the first tier makes use of Artificial Neural Network (ANN) to predict the chances of a person suffering from stroke. The ANN is trained the using the values of various risk factors of stroke of several patients who had stroke. Once a person is classified as having a high risk of stroke, s/he undergoes another the tier-2 classification test where his/her neuro MRI (Magnetic resonance imaging) is analysed to predict the chances of stroke. The tier-2 uses Non-negative Matrix Factorization and Haralick Textural features for feature extraction and SVM classifier for classification. We have obtained an accuracy of 96.67% in tier-1 and an accuracy of 70% in tier-2.
