Intelligent classification of Heart Disease using Neighboring Support Vector Classifier
This paper proposes a new algorithm in order to improve the classification phase of medical images analysis to detect Heart Disease.our algorithms, the Neighboring Support Vector Classifier NSVC, is a new supervised learning algorithm with better performances in medical diagnosis. The approach consists on the combination different classifiers from different families (unsupervised classification: Fuzzy C-Means and supervised: SVM). The application
of this algorithm on two well-known real data sets allow us to validate the method and show its interest in the context of image based medical diagnosis. The best accuracy obtained are 98,5% for SPECT Heart data set and 97.41% for Statlog Heart Data set. Many experiments are performed to confirm the robustness of NSVC using different datasets.
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