Identification of Potential Diabetes Mellitus Through Iris Images

Diagnosis of a disease is usually carried out in a laboratory test, where this test requires a lot of money and time. As time goes by in the world of health, there is one method that can be used to detect the level of condition in the body by recognizing the patterns that form on the iris of the eye or better known as Iridology. By using digital image processing, the disease diagnosis process using the iridology method can be carried out. The aim of this program is to identify diabetes milletus by processing digital images in the form of iris images using Matlab . The research data used comes from the open source website, namely Github , which consists of diabetic iris image data and normal iris image data. Data is taken in the form of digital images. In this research there are several stages, including pre-processing, feature extraction, and classification. The feature extraction process is based on statistical characteristics (contrast, correlation, energy, homogeneity) from the iris image, then classified using the K-Nearest Neighbors (KNN) algorithm and Support Vector Machine (SVM) as a comparison algorithm. The results obtained from this research show that K-NN has better performance with accuracy values ​​of 84%, sensitivity 72%, and specificity 96%.