Non-Invasive Diagnosis of Type II Diabetes Using Iris-Based Machine Learning Models
Non-Invasive Diagnosis of Type II Diabetes Using Iris-Based Machine Learning Models |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-5 |
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| Year of Publication : 2025 | ||
| Author : Alaa Abdulkareem Ahmed, Mohammad Tariq Yaseen |
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| DOI : 10.14445/22315381/IJETT-V73I5P117 | ||
How to Cite?
Alaa Abdulkareem Ahmed, Mohammad Tariq Yaseen, "Non-Invasive Diagnosis of Type II Diabetes Using Iris-Based Machine Learning Models," International Journal of Engineering Trends and Technology, vol. 73, no. 5, pp.186-198, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I5P117
Abstract
The increasing occurrence of Type II Diabetes Mellitus (T2DM) due to lifestyle changes has required the development of non-invasive diagnostic methods. This study explores the potential of iridology, another diagnostic approach, joined with machine learning (ML) algorithms to detect T2DM based on a precise Region of Interest (ROI) in the right iris. This work introduces two ML-based classification methods. The first method employs multiple ML models with changing K-fold cross-validation (ranging from 2 to 20 folds), achieving a maximum classification accuracy of 78.5% at 5-fold using Support Vector Machine (SVM) and Binary Generalized Linear Model (GLM) Logistic Regression. The second method employs Principal Component Analysis (PCA) to enhance feature selection, improving accuracy to 82.2% by training predictions from two initial classifiers-Coarse Decision Tree (74.8% at 14-fold, PCA variance 97%) and Linear Discriminant Analysis (77.6% at 9-fold, PCA variance 100%)-before refining classification with a Binary GLM Logistic Regression model. The proposed approach offers a promising, non-invasive alternative for early diabetes detection using iris analysis and Artificial Intelligence (AI).
Keywords
Artificial intelligence, Diabetes detection, Iris-based diagnosis, Machine Learning, Non-Invasive.
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