Smart Iridology: Deep Learning for Predictive Health Insights

This study aims to develop a reliable, non-invasive method for predicting early health issues through iridology by leveraging computer vision and deep learning techniques. Iridology, which involves the analysis of iris patterns, is explored for its potential to provide early indications of health conditions. The methodology involved capturing high-quality iris images using a specialized camera, followed by processing and analysis using advanced computer vision techniques. A deep learning model was implemented to detect early stages of conditions such as diabetes, hypertension, and heart diseases. The model achieved an average accuracy of 93.7%, demonstrating superior performance in precision, sensitivity, and specificity compared to other leading machine learning approaches. These findings suggest that the proposed method could serve as a robust tool for early health issue detection, with implications for enhancing public health outcomes and reducing healthcare costs.