Abstract
Data mining techniques have been widely used in clinical decision support systems for detection and prediction of various diseases. As heart disease is the leading cause of death for both men and women, detection and prediction of the heart disease is one of the most important issues in medical domain and many researchers developed intelligent medical decision support systems to improve the ability of the CAD systems in diagnosing heart disease. However, there are almost no studies investigating capabilities of hybrid ensemble methods in building a detection and prediction model for heart disease. In this work, we investigate the use of hybrid ensemble model in which a more reliable ensemble than basic ensemble models is proposed and leads to better performance than other heart disease prediction models. To evaluate the performance of proposed model, a dataset containing 278 samples from SPECT heart disease database is used that after applying the model on the data, 96% of classification accuracy, 80% of sensitivity and 93% of specificity are obtained that indicates acceptable performance of the proposed hybrid ensemble model in comparison with basic ensemble model as well as other state of the art models.
Highlights
The World Health Organization has estimated that 12 million deaths occur worldwide, every year due to the heart diseases [1]
In the last few decades many computational tools have been designed to improve the abilities of physicians for making decisions about condition of disease in their patients [2], low performance of current heart disease detection models is remained a matter of concern and potential of data mining algorithms which are motivated by the need of an expert system, have not be highlighted in any research yet
In part 2, results of applying different classifiers as fuser classifier will be investigated and a comparison between single base classifier model introduced in Section VI and the proposed hybrid ensemble model will be discussed in part 3
Summary
The World Health Organization has estimated that 12 million deaths occur worldwide, every year due to the heart diseases [1]. Artificial intelligence algorithms have great potential for exploring the hidden patterns in the datasets of the various disease related subjects by adjusting the data mining model for utilizing such patterns for clinical diagnosis [1] and this potential has led to building expert systems that can be used in CAD systems for prediction and detection of diseases in patients. One of the concepts that have been emerged in recent years is the idea of combining classifiers as a new direction for the improvement of the performance of individual classifiers [3] These classifiers could be based on a variety of classification methodologies and could achieve different rate of correctly classified samples. We show that the proposed method is capable of being used as a more powerful tool to assist the medical doctor in detection and prediction of the heart disease than the basic ensemble models as well as other state of the art models. The present study is focused on the idea of hybrid ensemble models and investigates the effectiveness of such models on the performance of a heart disease detection and prediction system
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