Hybrid Naïve Possibilistic Classifier for heart disease detection from heterogeneous medical data

This paper investigates a Hybrid Naïve Possibilistic Classifier (HNPC) to detect the presence of heart disease from the heterogeneous data (numerical and categorical) of the Cleveland dataset. The proposed classifier stands for the hybridization of two versions of Naïve Possibilistic Classifier (NPC) which have been recently applied on numerical and categorical data, respectively. To estimate possibility beliefs from data, each one of these two versions calls the probability-possibility transformation method of Dubois et al. Later, two fusion steps are performed to make decision. In the first fusion, possibility values are combined within each classifier using the product and the minimum operators for numerical and categorical data, respectively. Then, these two rules are investigated in the second fusion step to combine possibilities assigned to each class. The obtained results show that the proposed HNPC outperforms the main classification techniques which have been used in recent related work.