DOI: 10.14457/tu.the.2015.1476
title: Establishing a classifier and estimation system of nitric oxide and hydrogen peroxide in lacunar stroke by support vector machines

description: In this study, we approached the performance of classification models by the support vector machines (SVMs) algorithms of NO and H2O2 concentration for assessment of a mechanism of lacunar stroke. Both NO and H2O2 are free radicals that play an important role as redox signaling, neuromodulator and oxidant in the system. We use cerebrovascular reactivity (CVR) test for assessment of cerebral reserve function and autoregulation in control vs LS. It is well known that the heart of SVMs classifier design is kernel function selection and adjustment of its parameters. We improve the most effective kernel function based on the best advantage of each single kernel model for training and testing these real-world medical data and then construct a novel integration of hybrid kernel function. For NO, the classifier performances by each single kernel function and hybrid kernel function are following: it showed 90.00% from polynomial kernel function in experiment phase and is followed by 88.00% and 84.00% from polynomial in recovery and basal phase, respectively. Considering the best parameters of most experimental results, they have shown classifier performance of NO by polynomial is high when the parameter  is odd numbers. The classification results of RBF in experiment phase is the best (94.00%), followed by results in basal (88.00%) and recovery (84.00%) phase, respectively. The classifier performance of RBF kernel is high when the parameter   is an even number. For sigmoid kernel function have shown low performance of the most  kernel function with highest classified accuracy is 78.00% in recovery phase and is followed by 74.00% and 58.00% in basal and experiment phase, respectively. The best parameters most experimental results have shown that classifier performance of sigmoid is high when the sum of a parameter  and  is odd numbers. An improvement of classifier performance for NO assessment,  when we vary the value of a parameter ( and ) to test the data sets in each phrase, the results of a hybrid kernel in experiment phase is the best (96.00%), followed by results in basal (94.00%) and recovery (92.00%) phase, respectively. The best values of both parameters resulting in the highest performance for classification of NO by hybrid kernel function are 0.5. Consider the values of parameters compared with the highest performance, low value of parameters is detected. In the case of H2O2, for polynomial, the highest classified accuracy is 80.00% in experiment phase and is followed by 72.00% and 50.00% in basal and recovery phases, respectively. The results of RBF in experiment phase is the best (90.00%), followed by results in basal (86.00%) and recovery (76.00%) phase, respectively. For sigmoid kernel function, it demonstrated the highest classified accuracy is 72.00% in basal phase and is followed by 58.00% and 54.00% in experiment and recovery phase, respectively. Contrast to NO, classifier performance of H2O2 by sigmoid kernel function is lower than those NO of all phases. For improvement of classifier performance for H2O2 assessment, the results of a hybrid kernel in experiment phase is the best (92.00%), followed by results in basal (86.00%) and recovery (82.00%) phase, respectively.In conclusion, the findings suggest that low value of right parameters  and  are able to improve performance of NO and H2O2 classification in lacunar stroke. Hybrid kernel function is the best giving the greatest classification accuracy for NO and H2O2 that play a novel role as neuromodulator and neurovascular oxidative stress.