Analysis of computational techniques for diabetes diagnosis using the combination of iris-based features and physiological parameters

• Published in: Neural computing & applications Vol. 31; no. 12; pp. 8441 - 8453
• Main Authors: Samant, Piyush, Agarwal, Ravinder
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Classifiers; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Diabetes; Diagnosis; Digital imaging; Feature extraction; Image processing; Image Processing and Computer Vision; Machine learning; Machine vision; Original Article; Parameter sensitivity; Physiology; Probability and Statistics in Computer Science; Subgroups; Iris; Physiological parameters; Iridology; Diabetes diagnosis; Classification
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04551-9

Digital image processing and advanced machine vision techniques are popular for the diagnosis of disease(s) in biomedical science. This paper presents a detailed comparative analysis of machine learning-based classification techniques to diagnose type 2 diabetes using the combination of iris-based features and physiological parameters. A set of 334 subjects are investigated which are divided into diabetic and non-diabetic groups. Moreover, the diabetic group is classified into three different subgroups according to the duration of the diabetic state. Statistical features, gray-level co-occurrence matrix, and gray-level run length matrix-based features are extracted from the specific areas of iris. Nine classifiers of different application areas are selected, and subsequently, six parameters (accuracy, precision, sensitivity, specificity, F -score, and area under the curve) of each classifier are analyzed. The analysis provided promising results with more than 95% of accuracy. The proposed technique can be used as a noninvasive and non-contact diabetes diagnosis tool which can help to find out the duration of diabetes in patients and the prevalence of diabetes.