Models of incremental concept formation
Artificial Intelligence
Communications of the ACM
An Artificial Neural Network that Models Human Decision Making
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Intelligent Systems for Business: Expert Systems with Neural Networks
Intelligent Systems for Business: Expert Systems with Neural Networks
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Intelligent Hybrid Systems
Stability analysis of T-S fuzzy models for nonlinear multiple time-delay interconnected systems
Mathematics and Computers in Simulation
Stability conditions of fuzzy systems and its application to structural and mechanical systems
Advances in Engineering Software
Predicting object-oriented software maintainability using multivariate adaptive regression splines
Journal of Systems and Software
Design of a hybrid system for the diabetes and heart diseases
Expert Systems with Applications: An International Journal
A new two-stage hybrid approach of credit risk in banking industry
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
SOFMLS: online self-organizing fuzzy modified least-squares network
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Support vector machine approach for longitudinal dispersion coefficients in natural streams
Applied Soft Computing
IEEE Transactions on Fuzzy Systems
Human factors of knowledge-sharing intention among taiwanese enterprises: A model of hypotheses
Human Factors in Ergonomics & Manufacturing
Identification of Extended Hammerstein Systems Using Dynamic Self-Optimizing Neural Networks
IEEE Transactions on Neural Networks
A study of a B2C supporting interface design system for the elderly
Human Factors in Ergonomics & Manufacturing
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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.