Hybrid intelligent modeling schemes for heart disease Classification
Abstract— we propose a modified Hybrid Naïve Possibilistic Classifier (HNPC) for heart disease detection from the heterogeneous data (numerical and categorical) of the Cleveland dataset. The proposed classifier is based on a different pattern with regard to our former HNPC which have been recently proposed to deal with the same problem. As HNPC, < Final Year Project > the modified classifier separates data into two subsets (numerical and categorical) and then estimates possibility beliefs using the two versions of the probability-possibility transformation method of Dubois ets al. for numerical and categorical data, respectively. However, unlike HNPC which is based on two fusion steps to make decision from possibility estimations, our new classifier performs a common fusion to combine these beliefs. During this fusion, the product and the minimum as main combination operators for possibility measures are investigated. Experimental evaluations on the Cleveland dataset show that the proposed modified HNPC may outperform the former HNPC as well as the main classification techniques which have been used in recent related work.
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