### I. Introduction

### II. Methods

### 1. Data

### 2. Synthetic Minority Over-sampling Technique

### 3. Feature Selection

*c*: the number of grade classification

*p*: the number of samples in class

_{i}*i*

*A*: attribute

*V*: a possible value for attribute

*A*

*Values(A)*: the set of possible values for attribute

*A*

*S*| : the number of samples for the value

_{v}*v*

*S*| : the total number of data samples

*Entropy*(

*S*) : entropy for samples that have value

_{v}*v*

*A*, where

_{i}*i*= 1,2, ...,

*N*

Wn= 0.0; FORi= 1 toNWn=Wn+W(A) IF_{i}Wn≥ Threshold THEN go to Step-5 ENDIFi=i+ 1 ENDFOR

*i*is selected, which attribute to 1, 2 ....

*i*.

### 4. Method Proposed

### 5. Performace Evaluation of the Proposed System

*k*-fold cross validation. The method simply divides the data into

*k*subsets, with

*k*= 2,3,4, ..., 10. Then, these

*k*subsetsare divided into two,

*k*– 1 subsets as training data, and a subset of data for testing.The performance of the system is assessed with reference to the confusion matrix table for multiclass, as shown in Table 2. Based on the table calculation system performance parameter value, the calculation is performed by counting TP, TN, FP, and FN results for each type/level. As an example we show the calculation of the values of TN, TP, FP, FN on healthy output, the calculation shown in Equations (7)–(10):

### III. Results

*p*-value) of the C4.5 system model. Testing was carried out using a

*t*-test with a confidence level of 95%, and the results are shown in Table 4. Fourth, we present a knowledge base which is modeled in a decision tree, which describes the relationship of attributes with coronary heart disease, as shown in Figure 2. The knowledge base shown in Figure 2 was obtained by a system with an mSMOTE combination model, with feature selection, IG and C4.5. The use of feature selection reduces the IG from 19 attributes to 16 attributes of coronary heart disease.

### IV. Discussion

*p*-value) with a 95% confidence level, as shown in Table 4, there was a significant difference among the model systems using mSMOTE+C4.5 compared with that using only C4.5. The results also show that the use mSMOTE provides significant improvement (

*p*< 0.05). This was also proved by Choi [19].

*p*-value in Table 4. The average performance of the system model with reference to the AUC value increased by 2.8%. It increased from 81.4% to 84.2% in comparison with the system that makes no use of feature selection, which 59.5% to 84.2% or 24.7% of the system models without mSMOTE. The AUC value was in the range of 80%–90% [20], which can be considered good.

IF thal>3 and years≤32.43and ca≤0 and tpeakbps≤159.07and restecg>1THEN num=3.

*p*< 0.05).

*p*-value is <0.05. In the research of Prabowo et al. [9], in addition to using feature selection CFS, motivated feature selection (MFS) was also used. In comparison with the combination of randomization, MFS, and J48 feature selection, the TPR and F-measure performance for all type/levels of the proposed system is relatively lower. Unfortunately, the method proposed by Prabowo et al. [9] uses the conversion of multiclass classification to binary. In [9], the classification process is done for each type/level, so there are five models of the diagnosis system. Tables 5 and 6 show the resulting system performance of all five models of diagnosis system. This is different from the multiclass approach. In this approach there is only one model of diagnosis system, with output there are 5 types/levels. In a binary classification approach, to obtain a single diagnosis system model with an output of five type/levels, The classifications must be compiled again into a single system. With a compiled system model, the performance may decline in comparison to that obtained using a model system for each diagnosis for each type/level.

*p*> 0.05) in comparison to the systems developed by Nahar et al. [8], Prabowo et al. [9], and Setiawan et al. [11], as shown in Tables 7 and 8. Using feature selection, the performance of the proposed system is better, with low significance (

*p*> 0.05) in comparison with previously proposed systems, as shown in Tables 7 and 9.