Performance Analysis of Multiclass Support Vector Machine Classification for Diagnosis of Coronary Heart Diseases

TL;DR

This paper evaluates the performance of various multiclass SVM algorithms in diagnosing different levels of coronary heart disease using UCI dataset, focusing on accuracy, precision, recall, and F-measure.

Contribution

It compares multiple multiclass SVM approaches for coronary heart disease diagnosis, providing insights into their effectiveness and performance metrics.

Findings

ECOC achieved the highest accuracy among tested methods.

OAO and BTSVM showed strong precision and recall.

Overall, multiclass SVM methods demonstrated reliable diagnosis capabilities.

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.