  Accurate diagnosis is required before performing proper treatments for
coronary heart disease. Machine learning based approaches have been proposed by
many researchers to improve the accuracy of coronary heart disease diagnosis.
Ensemble learning and cascade generalization are among the methods which can be
used to improve the generalization ability of learning algorithm. The objective
of this study is to develop heart disease diagnosis method based on ensemble
learning and cascade generalization. Cascade generalization method with loose
coupling strategy is proposed in this study. C4. 5 and RIPPER algorithm were
used as meta-level algorithm and Naive Bayes was used as baselevel algorithm.
Bagging and Random Subspace were evaluated for constructing the ensemble. The
hybrid cascade ensemble methods are compared with the learning algorithms in
non-ensemble mode and non-cascade mode. The methods are also compared with
Rotation Forest. Based on the evaluation result, the hybrid cascade ensemble
method demonstrated the best result for the given heart disease diagnosis case.
Accuracy and diversity evaluation was performed to analyze the impact of the
cascade strategy. Based on the result, the accuracy of the classifiers in the
ensemble is increased but the diversity is decreased.
