The Hybrid Logistics Regression-Artificial Neural Network and Multivariate Adaptive Regression Splines-Artificial Neural Network Modeling Schemes for Heart Disease Classification
Heart disease is the leading cause of death in men and women in most countries in the world. People must pay attention to heart disease risk factors. Although genetics plays a role, some lifestyle factors are also crucial for heart disease risk. The traditional approaches use thirteen risk factors or explanatory variables to classify heart disease. Diverging from existing approaches, the present study proposes a new hybrid modeling scheme to obtain different sets of explanatory variables, and the proposed hybrid models are able to effectively classify heart disease. The proposed hybrid models consist of the logistics regression (LR), multivariate adaptive regression splines (MARS) and the artificial neural network (ANN) components. The initial stage is to use the LR or MARS technique to reduce the set of explanatory variables. The remaining variables are then served as inputs to the ANN in the second stage. A real data set of heart disease is used for demonstration of the development of the proposed hybrid models. The modeling results reveal that the proposed hybrid scheme is able to effectively classify heart disease and outperform the typical, single stage ANN method.
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Document Type: Research Article
Publication date: November 1, 2013
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