  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.
