  Background and purpose: Heart disease has been one of the most important
causes of death in the last 10 years, so the use of classification methods to
diagnose and predict heart disease is very important. If this disease is
predicted before menstruation, it is possible to prevent high mortality of the
disease and provide more accurate and efficient treatment methods. Materials
and Methods: Due to the selection of input features, the use of basic
algorithms can be very time-consuming. Reducing dimensions or choosing a good
subset of features, without risking accuracy, has great importance for basic
algorithms for successful use in the region. In this paper, we propose an
ensemble-genetic learning method using wrapper feature reduction to select
features in disease classification. Findings: The development of a medical
diagnosis system based on ensemble learning to predict heart disease provides a
more accurate diagnosis than the traditional method and reduces the cost of
treatment. Conclusion: The results showed that Thallium Scan and vascular
occlusion were the most important features in the diagnosis of heart disease
and can distinguish between sick and healthy people with 97.57% accuracy.
