An expert system based on wavelet decomposition and neural network for modeling Chua's circuit
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Diagnosis of valvular heart disease through neural networks ensembles
Computer Methods and Programs in Biomedicine
A comparison of multiple classification methods for diagnosis of Parkinson disease
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
International Journal of Computational Intelligence Studies
Computers in Biology and Medicine
Diagnosis of hypoglycemic episodes using a neural network based rule discovery system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
Expert Systems with Applications: An International Journal
Effective Diagnosis of Coronary Artery Disease Using The Rotation Forest Ensemble Method
Journal of Medical Systems
Association rule mining to detect factors which contribute to heart disease in males and females
Expert Systems with Applications: An International Journal
Neuro-fuzzy integrated system with its different domain applications
International Journal of Intelligent Systems Technologies and Applications
Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis
International Journal of Knowledge Discovery in Bioinformatics
Using decision tree for diagnosing heart disease patients
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
A survey of multiple classifier systems as hybrid systems
Information Fusion
Ensemble methods for advanced skier days prediction
Expert Systems with Applications: An International Journal
A threshold fuzzy entropy based feature selection for medical database classification
Computers in Biology and Medicine
Hi-index | 12.06 |
In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.