Binary Research Papers - Academia.edu (original) (raw)

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), OneAgainst-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 sickhigh
and sick-serious, and ECOC- SVM algorithm gave the highest recall 0.0% for sick-medium and sickserious.
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 BTSVM,
OAO-SVM, DDAG-SVM to diagnosis the type or level of coronary heart disease provides better
performance, than the binary classification approach.