Iridology Achieves 91% Accuracy in Detecting Heart Disease

Nowadays, technology to detect heart conditions from an early stage has been carried out by scientists in the medical field and computing technology. One of them is knowing the heart condition through the eye's iris, which is directly connected to the brain

The iris is one of the unique organs in the human body. Iris is usually used as electronic security or biometric identification system. Nowadays, the research on the iris in the medical field is increasingly widespread and is being seriously studied by experts. Iridology is based on the analysis of one of the most complex tissue structures contained in the iris. This method can determine the condition of organs and systems in the body from the marks on the iris. Iridology helps to identify existing or any potential problems in a particular organ

In this study, iris samples were collected from healthy individuals and patients with coronary heart disease to explore new directions and methodologies in diagnosing coronary heart disease with computer imaging and machine learning techniques. The determination of the iris section has been carried out automatically using the Hough transformation method, which is considered more efficient in reading multiple iris samples

The paper presents a method for identifying coronary heart disease (CHD) using iridology, an alternative diagnostic approach that analyzes the human iris. By applying image processing techniques like the Gray Level Co-occurrence Matrix (GLCM) for feature extraction and using Support Vector Machine (SVM) for classification, the system can detect abnormal heart conditions with a high level of accuracy. The best results were achieved using the Gaussian kernel with a 91% accuracy rate. This approach helps provide an early diagnosis of heart conditions through non-invasive iris analysis.

Previous studies have used the iris as an early diagnosis in detecting abnormal conditions in human organs. The use of different methods leads to different results in identification accuracy.