Machine learning techniques for medical diagnosis of diabetes using iris images
Abstract
Background and Objective
Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for disease diagnosis is reliable and fastest growing field in biomedical. Proposed model is an attempt to evaluate diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes using soft computing methods.
Methods
Investigation was performed over a close group of total 338 subjects (180 diabetic and 158 non-diabetic). Infra-red images of both the eyes were captured simultaneously. The region of interest from the iris image was cropped as zone corresponds to the position of pancreas organ according to the iridology chart. Statistical, texture and discrete wavelength transformation features were extracted from the region of interest.
Results
The results show best classification accuracy of 89.63% calculated from RF classifier. Maximum specificity and sensitivity were absorbed as 0.9687 and 0.988, respectively.
Conclusion
Results have revealed the effectiveness and diagnostic significance of proposed model for non-invasive and automatic diabetes diagnosis.
Computer Methods and Programs in Biomedicine
Volume 157, April 2018, Pages 121-128
Reference: https://www.sciencedirect.com/science/article/pii/S0169260717304649
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