Identification of Coronary Heart Disease through Iris using Gray Level Co-occurrence Matrix and Support Vector Machine Classification


Now-a-days, coronary heart disease is one of the deadliest diseases in the world. An unfavorable lifestyle, lack of physical activity, and consuming tobacco are the causes of coronary heart disease aside from genetic inheritance. Sometimes the patient does not know whether he has abnormalities in heart function or not. Therefore, this study proposes a system that can detect heart abnormalities through the iris, known as the Iridology method. The system is designed automatically in the iris detection to the classification results. Feature extraction using five characteristics is applied to the Gray Level Co-occurrence Matrix (GLCM) method. The classification process uses the Support Vector Machine (SVM) with linear kernel variation, Polynomial, and Gaussian to obtain the best accuracy in the system. From the system simulation results, the use of the Gaussian kernel can be relied on in the classification of iris conditions with an accuracy rate of 91%, then the Polynomial kernel accuracy reaches 89%, and the linear kernel accuracy reaches 87%. This study has succeeded in detecting heart conditions through the iris by dividing the iris into normal iris and abnormal iris.


This study has proposed a new method to determine the condition of the heart through the iris using the SVM classification with variations of the linear kernel, polynomial kernel, and Gaussian kernel. The use of GLCM characteristics as feature extraction has an essential role in the classification process. The main contribution in this study is not only limited to determining heart health conditions through the iris but also contributes to the automatic processing of the iris with CHT. The proposed system in determining the heart condition automatically is to optimize the classification by using angle 0° and 90° on GLCM with SVM classification on the Gaussian kernel to obtain a high level of accuracy. In ongoing research, the iris database can be added to improve the classification to make it more accurate. Different extraction methods can be used to get the results of image extraction with a smaller size so that it can increase the system’s speed in iris identification.


This work was supported by the Kariadi Hospital in Semarang and the Department of Engineering at the Universitas Palangkaraya and the Department of Engineering at the Universitas Tanjungpura, Indonesia.

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