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


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.

Neural Computing and Applications


, Volume 31, Issue 12, pp 8441–8453

  • Piyush Samant
  • Ravinder Agarwal

Thapar Institute – India

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