Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis
Ferdi Özbilgin1, Çetin Kurnaz2, Ertan Aydın3
1Department of Electrical and Electronic Engineering, Giresun University, Giresun 28200, Turkey.
Insights
This study introduces a novel, non-invasive method for diagnosing Coronary Artery Disease (CAD) using iris image analysis. The technique achieves 93% accuracy, potentially simplifying early detection and supporting telemedicine for heart conditions.
Area of Science:
- Ophthalmology
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary Artery Disease (CAD) is a leading cause of mortality due to narrowed coronary vessels restricting heart blood flow.
- Early diagnosis and treatment of CAD are crucial for improving patient prognosis and preventing disease progression.
- Current diagnostic methods for CAD can be invasive or require specialized equipment.
Purpose of the Study:
- To propose and evaluate a novel, non-invasive method for diagnosing Coronary Artery Disease (CAD) using iris image analysis.
- To investigate the efficacy of combining iridology with advanced image processing techniques for CAD detection.
- To establish a preliminary diagnostic tool for CAD that complements existing medical imaging and testing modalities.
Main Methods:
- Iris images from 198 volunteers (94 with CAD, 104 without) were analyzed.
- Iris images were processed using integral differential operator and rubber sheet methods, with the heart region cropped.
- Feature extraction involved wavelet transform, statistical analysis, GLCM, and GLRLM, followed by Support Vector Machine (SVM) classification.
Main Results:
- The proposed method achieved a 93% accuracy rate in predicting Coronary Artery Disease (CAD).
- The model demonstrated high performance across various evaluation metrics including sensitivity, specificity, and AUC.
- Preliminary diagnosis of CAD was achieved through iris analysis, potentially reducing the need for traditional tests.
Conclusions:
- Iris image analysis offers a promising non-invasive approach for the preliminary diagnosis of Coronary Artery Disease (CAD).
- This method can potentially serve as a valuable tool in telemedicine systems for remote CAD screening and diagnosis.
- Further research can explore integration into broader telemedicine platforms for accessible cardiovascular health monitoring.
Abstract:
Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model's performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.
More Related Videos
07:51Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
13:07Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:30Coronary Artery Disease I: Introduction
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:27Coronary Artery Disease V: Interprofessional Care
01:26Coronary Artery Disease IV: Preventive Measures
01:26Coronary Artery Disease II: Pathophysiology