An application of black hole algorithm and decision tree for medical problem

In this study, we propose a novel method for medical data classification, it is the integration of new heuristic algorithm that get inspired the black hole phenomenon called as Black Hole Algorithm (BHA) and decision tree (C4.5). To evaluate the effectiveness of our proposed method, it is implemented on 2 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. The results of BHA + C4.5 implementation are compared to seven well-known benchmark classification methods (support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Linear Discriminant Analysis (LDA), Self-Organizing Map and Naive Bayes). Repeated five-fold cross-validation method is used to justify the performance of classifiers. Two criteria are used for model evaluation. They are Matthews´ Correlation Coefficient (MCC) and Accuracy. Experimental results show that our proposed method outperforms the other classification methods in MCC index and have higher accuracy after SVM and LDA classifiers.