Hybrid intelligent modeling schemes for heart disease classification

Abstract

Heart disease is the leading cause of death among both men and women in most countries in the world. Thus, people must be mindful of heart disease risk factors. Although genetics play a role, certain lifestyle factors are crucial contributors to heart disease. Traditional approaches use thirteen risk factors or explanatory variables to classify heart disease. Diverging from existing approaches, the present study proposes a new hybrid intelligent modeling scheme to obtain different sets of explanatory variables, and the proposed hybrid models effectively classify heart disease. The proposed hybrid models consist of logistic regression (LR), multivariate adaptive regression splines (MARS), artificial neural network (ANN), and rough set (RS) techniques. The initial stage of the proposed process includes the use of LR, MARS, and RS techniques to reduce the set of explanatory variables. The remaining variables are subsequently used as inputs for the ANN method employed in the second stage. A real heart disease data set was used to demonstrate the development of the proposed hybrid models. The modeling results revealed that the proposed hybrid schemes effectively classify heart disease and outperform the typical, single-stage ANN method.