svm classification to distinguish parkinson disease patients
In this paper we have discussed the importance of data mining in the field of bioinformatics and various subfields of bioinformatics in which data mining has shown its great impact. Using a data mining tool, Weka, we pre- process the dataset on which we have worked and then using one of the classification methods i.e. Support Vector Machine method (SVM), we distinguished people with Parkinson's disease from the healthy people. Appling libsvm we have tried to find the best possible accuracy on different kernel values for the given dataset. We study the ROC curve variation, and the way the value of true positive and false positive rates changes with increasing number of the cross validation folds.
kernel roc curve maximum marginal hyperplane (mmh) true positive rate (tpr) support vector machine (svm) classifier false positive rate (fpr) parkinson disease (pd)