Extreme Learning Machine for Iris-Based Diabetes Detection

This study proposes a methodology for iris-based diabetes detection in 130 subjects, in which geometric transformations and changes in brightness and contrast were applied to increase to 1300 images, and a selection of 10% of the pixels were selected, and 13 principal components were used to feed an Extreme Learning Machine with the Adam optimization algorithm, a learning rate of 0.01, 256 neurons in the hidden layer, and a batch size of 128. After performing five-fold cross-validation, the results demonstrated balanced performance, with a mean accuracy of 0.9992, mean F1-score of 0.9988, and mean AUC of 0.9999 for diabetes detection.