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The morality rates from diseases are much greater than those of accidents and natural disasters. The World Health Organization estimates that 17 million deaths worldwide each year occur due to cardiovascular diseases (Bonow, Mann, Zipes, & Libby, 2012). A major type of such diseases is coronary artery disease (CAD), which is reported to account for 7 million deaths over the world per annum (Bonow et al., 2012).
Mining is the extraction of knowledge from a set of data. In other words, data mining is a process that uses intelligent techniques whereby knowledge of a set of data can be extracted (Bickel & Scheffer, 2004).
Angiography is the modality of choice for the diagnosis of CAD. Angiography determines the location and extent of the stenotic arteries; nevertheless, its high costs and risks for the patient have prompted researchers to seek less expensive and more effective methods with the aid of data mining. Moreover, cost-sensitive algorithms can be of huge value in this field as misclassification of diseased or healthy patients has different costs. Pedreira et al. (2005), using the Neural Network on UCI (UC Irvine Machine Learning Repository, 2012) datasets, attained an accuracy rate of 80% for CAD diagnosis. Das et al. (2009) applied the Neural Network on the datasets of Cleveland (UC Irvine Machine Learning Repository, 2012) and reported an accuracy rate of 89.01%. Babaoglu et al. (2010) utilized the Support Vector Machine (SVM) algorithm on an exercise test data and achieved an accuracy rate of 79.17%. Tsipouras et al. (2008) used the Fuzzy Model to detect CAD. Itchhaporia et al. (1995) drew upon the Neural Network to analyze an exercise test data for the diagnosis of CAD. Polat et al. (2007) by using fuzzy systems and KNN reached the accuracy of 87% for CAD diagnosis. Alizadehsani et al. (2012) proposed a new ensemble algorithm which diagnoses CAD by 88.5% accuracy. Lee et al. (2008) used Heart Rate Variability (HRV) features for diagnosing CAD. Karaolis et al. (2010) and Snirivas et al. (2010) used C4.5 and naïve bayes algorithm respectively to diagnose CAD.
One of the purposes of the present study was to investigate rule based classifiers for CAD diagnosis. Resulted in low specificity rule based classifiers, other methods were sought in this paper. We use MetaCost, which is a cost-sensitive (Domingos, 1999) algorithm, so as to distinguish CAD patients from healthy individuals. The Sequential Minimal Optimization (SMO) (Platt, 1998), Naïve Bayes (Caruana, & Niculescu-Mizil, 2006), C4.5 (Quinlan, 1996), Support Vector Machine (SVM) (Ben-Hur & Weston, 2010), and K-Nearest Neighbors (KNN) (Larose, 2005) algorithms were employed to analyze the Z-Alizadeh Sani dataset with no feature normalization. The performance of all the mentioned algorithms was calculated using 10-fold cross-validation. This dataset contains information on 303 random visitors to Rajaei Cardiovascular, Medical and Research Center in Tehran, Iran. The dataset was enriched with three created features extracted from the other features prior to the application of the cost-sensitive algorithms on the datasets. The effect of the created features was investigated both theoretically and practically. First, an assumption was made about the created features. Then a lemma was stated to provide a subset of sample which satisfied the assumption. Afterwards, another lemma was presented using assumption 1 in order to discuss the effectiveness of the created features. In the experiments, the correctness of assumption 1 and the effectiveness of the created features were studied. As a result, high rates of both accuracy and sensitivity were obtained which, to the best of our knowledge, are superior to the existing studies in this area.