PERFORMANCE ANALYSIS OF MULTICLASS SUPPORT VECTOR MACHINE CLASSIFICATION FOR DIAGNOSIS OF CORONARY HEART DISEASES

International Journal on Computational Science & Applications (IJCSA) Vol.5, No.5,October 2015
DOI:10.5121/ijcsa.2015.5503 27
PERFORMANCE ANALYSIS OF MULTICLASS
SUPPORT VECTOR MACHINE CLASSIFICATION FOR
DIAGNOSIS OF CORONARY HEART DISEASES
Wiharto1,2
,Hari Kusnanto2
and Herianto3
1
Department of Informatic, Sebelas Maret University,Indonesia,
2
Department of Biomedical Engineering (BME),Gadjah Mada University, Indonesia and
3
Department of Mechanical and Industrial Engineering,Gadjah Mada University,
Indonesia
ABSTRACT
Automatic diagnosis of coronary heart disease helps the doctor to support in decision making a diagnosis.
Coronary heart disease have some types or levels. Referring to the UCI Repository dataset, it divided into 4
types or levels that are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models can be
analyzed with multiclass classification approach. One of multiclass classification approach used, one of
which is a support vector machine (SVM). The SVM use due to strong performance of SVM in binary
classification. This research study multiclass performance classification support vector machine to
diagnose the type or level of coronary heart disease. Coronary heart disease patient data taken from the
UCI Repository. Stages in this study is preprocessing, which consist of, to normalizing the data, divide the
data into data training and testing. The next stage of multiclass classification and performance analysis.
This study uses multiclass SVM algorithm, namely: Binary Tree Support Vector Machine (BTSVM), One-
Against-One (OAO), One-Against-All (OAA), Decision Direct Acyclic Graph (DDAG) and Exhaustive
Output Error Correction Code ( ECOC). Performance parameter used is recall, precision, F-measure and
Overall accuracy. The experiment results showed that the multiclass SVM classification algorithm with the
algorithm BT-SVM, OAA-SVM and the ECOC-SVM,gave the highest Recall in the diagnosis of type or
healthy level with a value above 90%, precision 82.143% and 86.793% F-measure,. For all kinds of
algorithms, except binary OAA-SVM algorithm gave the highest recall 0.0% for the type or level of sick-
high and sick-serious, and ECOC- SVM algorithm gave the highest recall 0.0% for sick-medium and sick-
serious. While the type or level other, the performance of recall, precision and F-measure between 20% -
30%,. The conclusion that can be drawn is that the approach to the multiclass classification algorithm BT-
SVM, OAO-SVM, DDAG-SVM to diagnosis the type or level of coronary heart disease provides better
performance, than the binary classification approach.
KEYWORDS
Automatic Diagnosis, Support Vector Machine, Coronary Heart Disease, Multiclass,Binary
1.INTRODUCTION
Coronary heart disease is a narrowing of the arteries disease. The disease causes high mortality in
a group of non-communicable diseases. Prevention and early detection of the disease is very
important to suppress the rate of deaths from the disease. Referring to the dataset UCI Repository
of coronary heart disease, heart disease grouped into 4 types or levels of a given symbol 1-4 [1].
According to [2] and [3], coronary heart disease classificated into 4 types or levels, namely low,
medium, high and serious [2] [3]. Based on these groupings, then the diagnosis to determine the
type or level of coronary heart disease is very important. Good diagnosis of the type or level of

28
coronary heart disease, will be used to provide appropriate action. Diagnosis of type or level of
coronary heart disease can use multiclass classification approach.
Research conducted by [4] that studied of the use of computational intelligence algorithms for the
diagnosis of coronary heart disease. The study did multiclass classification conversion to binary
classification. Selection of the classification, due to the classification method SMO (Support
Vector Machine Optimization) has a good ability in binary classification [4]. Classification is
done by creating a positive and negative labels for each level. If we would classify a low level,
then the low-level data is given a positive label, while another level in addition to the low level
given the negative label. The method makes use of the classification into binary classification,
because it only detects positive or negative. Results of testing with 10-fold crossvalidation
method shows good accuracy, but very low for a true positive (TP) and F-measure for low-level,
high, medium and serious. Computational intelligence algorithms tested in the study were naive
Bayesian, SMO, IBK, AdaaBoostM1, J48 and PART.
Subsequent research conducted by [5]. The study uses a classification approach and
computational intelligence algorithms similar to research done by [4]. Research conducted by [5]
using 10-fold cross-validation with iterations as much as 100 times, while testing the significance
of differences between computational intelligence algorithms using t-test. The test results showed
that the SMO gives better performance of the other algorithm. Other similar studies have also
been conducted by [6]. This study is similar to that done before, by using the binary classification
approach. This study adds randomize the process prior to the 10-fold cross validation. The
processes are carried out as many as 100 times, while the end result is the average yield of 100
times the [6]. Multiclass clasification conversion approach to binary classification also performed
by [7], its research emphasize banchmarking feature selection methods, computational
intelligence algorithm C4.5 and Naive Bayesian.
The next approach used is multiclass classification approachment. Research has been done by [8],
which is an intelligence system for predictive type or level of coronary heart disease using
methods Weighted Associative Classifier (WAC). The method is able to provide improved
performance, when compared with the other associative classification methods [8]. Following
research conducted by [9], is a research proposes a hybrid classification with a genetic algorithm,
a modified k-NN and back propagation neural network. Testing of the system by using one
dataset coronary heart disease cleveland UCI Repository. The test results demonstrate the
accuracy reached 62.1%. The result is better than multiclass classification algorithms like naive
Bayesian tested and k-NN [9]. Furthermore, research conducted by [10] and [11], which used
neural network to classify heart disease [10] [11]. In the study of heart disease is classified into
four classes, namely normal person, a first stroke, a second stroke and end of life. In the study
conducted by [11], in addition to using 13 variables from the dataset cleveland UCI Repository,
also using variable plus namely obesity and smoking. The addition of these variables make better
neural network performance in terms of accuracy.
The studies that have been conducted by [4], [5] and [6] showed that the SMO algorithm gives
good performance. The downside of these studies did not use the approach of multiclass
classification Support Vector Machine. Method of Support Vector Machine (SVM) is a powerful
binary classification. SVM also developed for the multiclass clacification. The classification
developed by the two approaches. They are the optimization of multi-class classification
problems and classification compiled from a binary SVM [12]. Research [12] describes some of
the settlement of multi-class classification, using a combination of a number of binary SVM.
These methods are One-Against-All (OAA), One-Against-One (OAO) and Directed Acyclic
Graph (DAG). The [13] also proposed multiclass SVM such as Binary Decision Tree archtecture

29
(Binary Tree SVM). Beside the [13], the [14] [15] [16] also developed multiclass SVM Error
Correction Output Code (ECOC).
Referring to the previous studies, this research will be to analyze the performance of multi-class
SVM classification for the diagnosis of type or level of coronary heart disease. Classification
algorithm used is Binary Tree SVM, OAO, OAA, DDAG and ECOC. Performance parameters in
the analysis is the recall, precision and F-measure. Research using cleveland dataset UCI
Repositor
2.MATAERIAL AND METHOD
2.1.Material
This study used a dataset of coronary heart disease from UCI Repository [1]. Coronary heart
disease data distributed into five types or levels. They are healthy, low-pain, pain-medium, ill-
high and ill-serious. Coronary heart disease data from the UCI Repository obtained from the
collection of Robert Detrano, MD, Ph.D., at VA Medical Center. The amount of data consists of
303 intance the data, the number of parameters 14, with one parameter as an indication of the
level of heart disease with a scale of 0-4. Influencing parameters is shown in detail in Table 1.
Table 1.Parameters that influence coronary heart disease.
No Parameters Description
1 Age Age (continues data) 29 to 77
2 Sex Sex : 1: man, 0: woman
3 Cp Chest pain type :
1 : Typical Angina
2 : atypical angina
3 : non-anginal pain
4 : asymtomatic
4 Trestbps Diastolic blood pressure (mm Hg)
5 Chol Cholesterol in mg/dl
6 Fbs Fasting blood suger >120 mg/dl, 1 : true, 0 : false
7 Restecg Resting ECG : 0 : Normal, 1 : ST-T abnormal, 2 : Left V. Hypertrophy
8 Thalach Maximum heart rate achieved
9 Exang Exercise induced angina (1=yes, 0=no)
10 Oldpeak ST depression induced byexercise relative to rest
11 Slope The slope of the peak exercise ST Segment : 1 : ups loping, 2: flat, 3 :
downsloping
12 Ca Number of major vessels colored by flouroscopy (0-3)
13 Thal Defect type : 3 : Normal, 6 : fixed defect, 7 : reversable defect
14 Num Heart disease (0-4) : 0=Healthy,1= low,2= medium, 3=high, 4=serious.

30
2.2.Method
This research method is divided into several stages, namely preprocessing, data sharing, training,
testing and performance analysis. Preprocessing stages, is to normalize the data, which bringing
the data into a certain scale. Normalization method used is using the Min-Max.
Figure 1. Coronary heart disease diagnosis system with multiclass SVM
The next stage after the preprocessing is to divide the data into two groups of data. They are
training and testing. The distribution of the data can be shown in Table 2.
Table 2. Dataset distribution Coronary Heart Disease
Level Training Testing
Healthy 114 50
Sick-Low 38 17
Sic- Medium 20 16
Sick-High 24 11
Sick-Serious 10 3
206 97
The next process is to training. Training done using some multiclass SVM algorithm are: BT,
OAO, OAA, DDAG, ECOC and as a comparison algorithm multiclass Multi Layer Perceptron
(MLP), Naive Bayesian (NB) and ESB (ensemble AdaaBoostM1) [17] [18]. After the training,
then performed testing using data that has been prepared. The parameters measured in testing
(testing) is recall (sensitifity), precision, F-measure and overall accuracy. Explanation for each
parameter with reference to Table 3 is as follows
a. Precision or confidence, is the number of positive samples were classified correctly
categorized as positive divided by the total data sample testing is classified as positive.

31
(1)
b. Recall or sensitifity, is the number of positive samples were classified correctly category
divided by the total positive samples in the data were categorized testing positive
(2)
c. F-Measure (F1), is the harmonic mean of Recall and Precision
(3)
d. Overall Accuracy, expressed in the following equation
(4)
Table 3. Confusion Matrics
Prediction Class
Actual Class Positif Negatif
Positif TP FN
Negatif FP TN
3.RESULT
Diagnosis of type or level of coronary heart disease is a problem that can be solved with some
approaches of multiclass classification. Research results multilcass SVM classification
performance analysis can be shown each level. Figure 2 is a comparison of the performance of
SVM multiclass in diagnosing healthy, or in any other sense diagnosis healthy or sick. BTSVM
algorithm performance, OAA, OAO, DDAG, ECOC provide almost the same performace, even
BTSVM, OAA and the ECOC provide recall performance above 90%. The precision and F-
measure reached above 80%. A good performance, when compared to the two levels of
diagnostic accuracy using SVM algorithm with a binary classification approach, as it typically
involves in previous studies [19] [20] [21] [4].
Figure 2: Performance diagnosis system at the level of Healthy
The results of further research, shown in Figure 3-6. The picture shows the performance of the
type or level of pain-low, ill-medium, ill-high and ill-serious with multiclass SVM algorithm,
Ensemble, naive Bayesian and multi-layer perceptron. Performance on the type or level sick-low

32
is very low even to below 40% better recall, precision and F-measure. Only one algorithm, that is
naive Bayesian recall that could reach above 50%. similar with that the type or level of sick-
medium, sick-high and sick-serious, the value of the three variables of performance below 50%,
which gives performance ECOC only recall, precision and F-measure above 60% for the type or
level of sick-High.
Figure 3: Performance diagnosis system at the level of Sick-Low
Figure 4: Performance diagnosis system at the level of Sick-Medium

33
Figure 5: Performance diagnosis system at the level of Sick-High
Figure 6: Performance diagnosis system at the level of Sick-Serious
The results of the next study is overall calculation accuracy of each algorithm. Fully shown in
Figure 7. Overall accuracy is generated no more than 70% for all algorithms, even just BTSVM
algorithm which has overall accuracy above 60%.
Figure 7.Overall Accuracy Multiclass Classification
4.DISCUSSIONS
Discussions in this study is that we are going to compare some research on the diagnosis of
coronary heart disease ever undertaken, using either a multiclass classification approach or
multiclass classification conversion to binary classification. First, the research that has been done
by [4]. The study uses a binary classifications approach, with reference to the strong performance
of the classification of SMO. Results of these studies was in good accuracy performance, for each
type or level, but has a value of TP (True Positive) and F-measure is very low. The condition
shows that high due to the high accuracy True Negatives (TN), or the system has a good ability in
classifying negative data, but less well in classifying the positive data. In addition, due to the
labeled amount of data is more negative than positive labeled. The results of a similar study was
also conducted by [6], which in this study has a high accuracy, but TP and F-measure low. When
compared with the research using SVM classification mutliclass, we take the same performance
parameters, namely the F-measure, show the performance of SVM multiclass give better results
from research conducted by [4] and [6] for type or level sick-low, sick-medium, sick-high and
sick-serious. Similar research has been done by [5], F-measure results better. The repair is done

34
by adding the number of iterations in the test using 10-fold cross validation. Multiclass SVM
classification performance comparison with previous studies, the full shown in Table 4.
Table 4. Comparison of F-measure binary classification approach with multiclass
Author Classification
Approach
Methode F-Measure (%)
Healthy Sick-
Low
Sick-
Medium
Sick-
High
Sick-
Serious
Nahar et,al [4] Binary SMO 81,800 0,000 0,000 0,000 0,000
Prabowo et.al[6] Binary SMO 84,200 0,000 0,100 3,500 0,000
Akrami et.al [5] Binary SMO* 87,000 90,000 93,000 94,000 98,000
This Study Multiclass BT 86,793 24,000 33,333 45,455 18,182
Multiclass OAA 86,793 21,739 32,000 0,000 0,000
Multiclass OAO 80,851 23,529 21,622 30,000 22,222
Multiclass DDAG 80,851 25,000 21,622 45,455 22,223
Multiclass ECOC 76,200 14,800 0,000 66,700 0,000
* with iterations as much as 100 times
The next comparison, are multiclass classification method. If we look at the performance of each
of multiclass SVM, the BT-SVM algorithm, OAO-SVM and SVM-DDAG recall that delivers
performance, precision, F-measure relatively stabil in any type or level. It is different for OAA-
ECOC-SVM and SVM there is some level of value 0. If we compare with SMO (SVM
Optimization) with a binary classification approach made [4] and [6], SVM multiclass have more
stable performance in any type or level of coronary heart disease. The downside of multiclass
classification is only able to give a good performance in diagnosing healthy, and not good for the
other type or level.
The next comparison is with previous studies that use multiclass classification approach. First,
research done by [8]. The study proposed a system of intelligence to predict heart disease using
Weighted Associative Classifier (WAC) [8]. The system was tested using multiple datasets, one
of the data is heart disease from UCI Repository [1]. The test results showed overall system
accuracy is still relatively low, that below 60% or below multiclass SVM performance. The study
did not measure performance for each type or level, so it can not be compared to each type or
level with multiclass SVM. Using the same concept, the [9] proposed a hybrid system. The
system is tested using several datasets, including UCI dataset Repository. The test results provide
performance overall accuracy of 62.1%. Hybrid system proposed in the study using a
combination of some of our algorithms, namely Genetic algorithm, neural netwok and modified
k-NN. Performance generated in these studies was slightly better than the performance of SVM
multiclass TB-SVM. Multilcass SVM research for the diagnosis of coronary heart defect,
resulting in overall performance 61.86% accuracy (BT-SVM) and the lowest 51.546% (OAO-
SVM). The poor performance due to the low capacity of the system in diagnosing the type or
level of low, medium, high and serious of coronary heart disease. Comparison multilcass SVM
classification system performance with some previous studies can be shown in Table 5.

35
Table 5. Overall Accuracy comparison multiclass classification
Author Methode Overall Accuracy (%)
Soni et.al [8] WAC 57,75
CBA 58,28
CMAR 53,64
CPAR 52,32
Salari et.al [9] Hybrid (ANN+GA+Modification k-NN) 62,10
This Study BT-SVM 61,86
OAA-SVM 56,70
OAO-SVM 51,546
DDAG-SVM 53,608
ECOC-SVM 58,763
NB 56,701
ESB 55,67
MLP 58,763
The next discussion is knowing the significance differences between the performance of
particular recall for the type or level of coronary heart disease. Significance testing the
performance difference between the type or level recall of coronary heart disease using t-test,
with a 95% confidence level (p-value <0.005). Significance test results are shown in Table 6.
From these results indicate that the performance difference between healthy recall the type or
level of low, medium, high and serious produce p-value <0.005 (with a 95% confidence level), it
means there are significant differences in the performance of recall. If we connect with the
amount of data used for training, the amount of data is more healthy than the type or level of low,
medium, high and serious. Referring to the research that has been done by [22], resulting in a
problem of data imbalance. A machine learning will yield a good prediction accuracy
classification of the class of data training with large numbers of members, while the number of
class members have a little less good accuracy [22].
Tabel 6. Significance test results using a t-test
No Comparison p-value
1 Healthy-Low 0,0000000240
2 Healthy-Medium 0,0000000005
3 Healthy-High 0,0000032178
4 Healthy-Serious 0,0000000354
5 Low-Medium 0,0677226456
6 Low-High 0,2222116996
7 Low-Serious 0,1024362015
8 Medium-High 0,0280130119
9 Medium-Serious 0,4727597556
10 High-Serious 0,0430968053

36
The significance of test results for type or level low, medium, high and serious p-value> 0.05,
meaning that there is not significant a differences. If we look at the data training used to have the
same relative amount of difference between the low-medium, low-high and low-serious, so its
performance is relatively the same. Type or level medium compared to high and serious, for
medium-high there was significant difference p-value <0.05. This is because the data type or level
high more than the type or level of medium. Referring to the research conducted by [22], a better
performance. Furthermore, for type or level high-serious also showed a significant difference, and
if we see the amount of data training occurred is also relatively large differences.
5.CONCLUSIONS
Research multiclass SVM classification performance analysis for the diagnosis of coronary heart
disease, some conclusions can be drawn. First, BT-SVM multiclass classification, OAO-SVM
and SVM-DDAG provide better performance than the classification of the binary classification
approach. Secondly, BT-SVM multiclass classification, OAO-SVM and DDAG-SVM provide a
relatively stabile performance for all levels, but there is a difference between the level of
performance that is significant at the level with minimal training data. Third, the occurrence of
imbalanced data on coronary heart disease data for sick-low levels, sick-medium, sick-high and
sick-serious, resulting in low performance of the system.
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