Coronary heart disease (CHD) has the highest mortality rate of all the non-communicable diseases throughout the world. Therefore, the prediction of CHD is necessary for reducing the management costs of CHD and for promoting health [1]. CHD is very dangerous because it is directly related to the patient's lifestyle; hence, prevention is important [2]. The two standard datasets used to predict the CHD risk level are the Framingham risk score (FRS) and prospective cardiovascular Münster (PROCAM) [3]. However, FRS and PROCAM are not tailored for Koreans; therefore, the accuracy of heart risk prediction using these methods is low when applied to Koreans [45].

Thus far, many previous studies have proposed methods to predict CHD using data mining, artificial intelligence, and machine learning techniques [67]. CHD prediction models based on data mining use various algorithms, such as artificial neural networks, decision trees, Bayesian theory, and genetic algorithms [8]. Anooj [9] proposed the generation of a fuzzy rule based on rule induction using decision trees to develop a clinical decision support system (CDSS) and predict the risk level. Khatibi and Montazer [10] developed a CHD risk prediction model based on the Dempster-Shafer evidence theory by designing a fuzzy-evidential hybrid inference engine using the FRS and PROCAM guidelines. Krishnaiah et al. [11] developed a CHD prediction system using a fuzzy K-NN classifier for measured values to remove uncertainty. CHD prediction models have been extended to a health management service model and a CDSS [12]. However, few studies have investigated the prediction of CHD in Koreans, which is an important requirement [5].

Therefore, it is necessary to develop a CHD prediction model for Koreans using data mining. In Korea, few studies have aimed to produce guidelines for CHD prediction thus far. Thus, rules based on guidelines are required, which should be produced using a data mining technique [13]. Certain biometric information related to CHD is also uncertain, so a solution is required to address this problem; fuzzy logic may reduce the uncertainty of medical informatics [14]. Additionally, the FRS guidelines, which have been used as a predictive model, are not appropriate for Koreans. Therefore, a new prediction model should be produced based on local clinical data to predict CHD in Koreans using decision tree rule induction [15].

In this study, the model was developed data mining-driven CHD prediction model using fuzzy logic and decision-tree. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to produce the proposed model [16]. Furthermore, rules were generated using the classification and regression tree (CART) of the decision tree technique [17], whereas a fuzzy logic approach was employed to address the uncertainty problem, which allowed CHD to be predicted.

The proposed CHD risk prediction model was implemented and evaluated. Figure 5 shows the experimental scenario tested. The final data from the KNHANES dataset (748 / 8,108 subjects) were categorized into a training set and a test set. The training set contained 525 subjects (70%), and it was used to create the rule induction and fuzzy membership functions as well as the classification prediction model. The test set contained 223 subjects (30%), and it was used to verify the proposed model.

IBM SPSS modeler 14.2 was used for rule induction. CART was also used for rule induction where the pruning severity was 75%, the minimum records per child branch was two, the boosting number was restricted to 10 for individual options, and the highest probability rule model was used. MATLAB R2009b with a fuzzy tool box was used to produce the fuzzy inference model. A confusion matrix was used to evaluate the predictive model [19]. Table 2 shows the confusion matrix.

The true positive (TP) value was the number of cases that correctly predicted CHD patients and the true negative (TN) value was the number of cases that correctly predicted healthy subjects as non-heart-disease patients. The false positive (FP) value was the number of cases that identified a patient as healthy who had CHD, and the false negative (FN) value was the number of cases that predicted that a patient had CHD who was healthy.

Our model was compared with previous results using an artificial neural network (ANN) [20], support vector machine (SVM) [21], logistics regression (LR) [22], and decision tree C5.0 [23] to evaluate the performance. Finally, it was compared with propose model. The confusion matrix and ROC curve results are shown in Tables 3 and 4, respectively.

The experimental results showed that the ANN, LR, and SVM had relatively high accuracy rates of 62.78%, 63.23%, and 67.71%, respectively, although they were lower than that of the proposed model because ANN and SVM only made observations at the learning level. C5.0, which are decision tree-based methods, yielded accuracy scores of 53.36%. The proposed model had accuracy and sensitive scores of 69.51% and 93.10%, respectively, which were higher than those of the other models. The higher accuracy and sensitivity of the proposed model can be attributed to the reduction of uncertainty achieve by using fuzzy logic. CART, which was used for rule induction, cannot process uncertainty adequately. The propose model performs better than ANN and SVM in terms of accuracy and sensitive is the highest reason, ANN and SVM learning and resoning about the complex relationship between the each training data; however, ANN and SVM do not resolve the problem of uncertainy. However, the propose model overcomes the problem of the uncertainty of the data by using fuzzy logic. However, specificity of proposed model is lower than that of the other models. Thus, future studies are required to develop a prediction model with higher specificity. The ROC curve result of the proposed model (0.594) was higher than that of the other models, and this can help in the decision support of the prediction of CHD.

This paper proposed a novel predictive model for CHD based on data derived from KNHANES-VI, which were collected by the Korea Centers for Disease Control and Prevention. The propose model decision supports the prediction of CHD by utilizing fuzzy logic and CART-based rule induction. Rule induction was performed using the KNHANES-VI datasets to generate the rules using the CART method. The prediction model used an inference model based on fuzzy logic. The rules were generated using a CART decision tree method, and fuzzy membership functions were created based on those used in previous case studies and FRS. A final dataset containing 748 subjects was selected from KNHANES-VI and used for the performance evaluation. The experimental results showed that the proposed model improved the prediction accuracy and sensitivity. Using the propose model is expected to offer decision support for CHD prediction.

Future research should focus on developing data mining based prediction methods that may also increase the accuracy and specificity of CHD prediction.