Non-invasive coronary artery disease identification through the iris and bio-demographic health profile features using stacking learning


This study proposes a non-invasive method for predicting Coronary Artery Disease (CAD) using iris analysis, patient data, and Machine Learning (ML), primarily with iris images. It involved 281 participants, comprising 155 CAD patients and 126 non-patient controls, with eye images and biodemographic data collected at a Cardiology outpatient clinic. The study explored three scenarios: Scenario-I focused on biodemographic data, Scenario-II on iris features, and Scenario-III combined iris images and data. Iris processing included location determination, normalization, and heart region selection, with image enhancement via adaptive histogram equalization. Feature extraction through a 2-level wavelet transform generated 272 attributes, including statistical, Gray Level Co-occurrence Matrix, and Gray Level Run Length Matrix features for eight subcomponents. Correlation-based selection identified the best features, and classification employed ML techniques and incorporated stacking learning to enhance the results. Scenario-I achieved the highest accuracy at 83.57% among all evaluated algorithms. In Scenario-II, the proposed algorithm consistently outperformed others, achieving 94.88% accuracy and strong performance in other metrics, highlighting its effectiveness. In Scenario-III, the algorithm maintained superiority with 96.07% accuracy, specificity, recall, and area under the curve values. The proposed algorithm consistently outperforms other methods across scenarios, indicating its potential for CAD diagnosis, making it a promising choice for future CAD systems. The proposed algorithm presents a novel approach to the preliminary diagnosis of CAD, eliminating the necessity for electrocardiography, echocardiography, or effort tests. It also enables seamless integration into telemedicine systems, allowing for tele-diagnosis to conduct preliminary assessments before routine clinical practice.

Authors: Ferdi Özbilgin a, Çetin Kurnaz b, Ertan Aydın c