Application of knowledge discovery process on the prediction of stroke. Issue 3 (May 2015) - Colak, Cemil; Karaman, Esra; Turtay, M. Gokhan
Highlights We predicted the outcome of stroke using knowledge discovery process (KDP) methods, Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models. The performance of the models, assessed as accuracy and area under curve (AUC), for predicting stroke was 85.9% and 0.928 for ANN, and 84.62% and 0.91 for SVM, respectively. KDP was used for providing better understanding of the stroke data, and reducing unnecessary processes to be performed. The proposed ANN model predicted successfully the stroke disease based on the selected predictors.