A Severity Diagnosis Method for Heart Disease based on Fusion Rough Sets

Abstract

In order to accurately diagnosis the severity of heart disease, we proposed a feature selection method by fusing rough sets. We firstly use genetic algorithm and heuristic algorithm based on attribute importance to select features and get the classification accuracy by support vector machine (SVM). Then, we use the two algorithms fused with rough set to select features, and get the classification again. After comparing the classification performances which obtained respectively, we find the classification accuracy of the heuristic algorithm based on attribute importance which fused with rough set has reached 89.125%, which is very close to 90.125% of the optimal solution. The results demonstrate that our method is effective and efficient.