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.

@article{Gunawan2022,
  title     = {Identification of Coronary Heart Disease through Iris using Gray Level Co-occurrence Matrix and Support Vector Machine Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {1},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Vincentius Abdi Gunawan and Leonardus Sandy Ade Putra and Fitri Imansyah and Eka Kusumawardhani},
  doi       = {10.14569/IJACSA.2022.0130177},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130177}
}