Objective: One of the important life-threatening ailment is stroke across the world. The current paper was performed to classify the outcome of stroke by using Least-Squares Support Vector Machines (LSSVMs) models.
Materials and Methods: The medical dataset related to stroke disease was achieved from the clinical database of the emergency medicine department. 28 predictors were recorded in raw dataset. For dimension reduction, correlations between input and target (stroke) variables were evaluated. Different LS-SVMs models were performed with radial basis function (RBF), linear and polynomial kernels. 5- fold cross-validation was used in composing stages to achieve the best model using all of the data. The accuracy and the Area under Receiver Operating Curve (AUC ROC) values were used for performance assessment.
Results: At first, feature selection stage was performed. 14 input variables were determined after this stage. Whole dataset was partitioned into 5 sub-datasets (D1, D2, D3, D4, D5) to use all data both training and testing. LS-SVMs models performance were evaluated by using 5-fold cross validation method. Accuracy and AUC values of the models were used as performance criteria. The best model performance was evaluated with LS-SVMs model using linear kernel. That model average accuracy was 86.6%. The best accuracy was evaluated with LS-SVM model using linear kernel on dataset D5 was 94%. As a consequence, the LS-SVMs model can be used for predicting the outcome of stroke.
Conclusion: The results point out that LS-SVMs with linear kernel have much more accuracy and AUC values for predicting stroke disease. The suggested LS-SVMs with linear kernel may produce beneficial prediction results related to stroke disease. In future studies, several data mining techniques may be tested and assembled for better classification performance of stroke disease.