Diagnosis of Diabetes Using Computer Methods: Soft Computing Methods for Diabetes Detection Using Iris

Authors: Piyush Samant, Ravinder Agarwal

Abstract:

Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: Complementary and alternative medicine, Iridology, iris, feature extraction, classification, disease prediction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129722

References:

[1] J. Beagley, L. Guariguata, C. Weil, and A. A. Motala, “Global estimates of undiagnosed diabetes in adults,” Diabetes Res. Clin. Pract., vol. 103, no. 2, pp. 150–160, 2013.
[2] T. Middle and E. Journal, “Effectiveness of complementary and alternative medicine – Call for a ‘black box’ research agenda,” Middle Eur. J. Med., vol. 7, no. 8, pp. 239–240, 2005.
[3] P. E. Harris, K. L. Cooper, C. Relton, K. J. Thomas, and P. Harris, “Prevalence of complementary and alternative medicine (CAM) use by the general population : A systematic review and update,” Int. J. Clin. Pract., vol. 66, no. 10, pp. 924–939, 2012.
[4] V. Thomas, N. V. Chawla, K. W. Bowyer, and P. J. Flynn, “Learning to predict gender from iris images,” 2007 First IEEE Int. Conf. Biometrics Theory, Appl. Syst., pp. 1–5, 2007.
[5] A. Bansal, R. Agarwal, and R. K. Sharma, “Predicting Gender Using Iris Images,” Res. J. Recent Sci., vol. 3, no. 4, pp. 20–26, 2014.
[6] S. Azilah and F. Paper, “Identification of vagina and pelvis from iris region using artificial neural network,” Teknologi, vol. 76, no. 7, pp. 91–95, 2015.
[7] A. Bansal, R. Agarwal, and R. K. Sharma, “Determining diabetes using iris recognition system,” Int. J Diabetes Dev Ctries, vol. 34, no. 4, pp. 432-438, 2015.
[8] L. W. Liam, Ali Chekima, L. Chung Fan and J. A. Dargham, “Tumor Detection using IRIS Pattern,” 4th Annual Seminar of National Science Fellowship, 2004.
[9] A. Helwan, “ITDS: Iris Tumor Detection System using Image Processing Techniques,” International J. Sci. Eng. Res., vol. 5, no. 11, pp. 76–80, 2014.
[10] I. P. Dody, L. I. Ketut, E. Purnama, and M. Hery, “Abnormal Condition Detection of Pancreatic Beta-Cells as the Cause of Diabetes Mellitus Based on Iris Image,” 2011 Int. Conf. Instrumentation, Commun. Inf. Technol. Biomed. Eng., pp. 150–155, 2011.
[11] J. F. Banzi and Z. Xue, “An Automated Tool for Non-contact, Real Time Early Detection of Diabetes by Computer Vision,” Int. J. Mach. Learn. Comput., vol. 5, no. 3, pp. 225–229, 2015.
[12] S. E. Hussein, O. a. Hassan, and M. H. Granat, “Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks,” Biomed. Signal Process. Control, vol. 8, no. 6, pp. 534–541, 2013.
[13] D. Hareva, S. Lukas, and N. Suharta, “The smart device for healthcare service: Iris diagnosis application,” in Eleventh International Conference on ICT and Knowledge Engineering, 2013, pp. 1–6.
[14] B. Jensen, “Iridology Chart.” (Online). Available: http://www.bernardjensen.com/. (Accessed: 01-Nov-2016).
[15] T. Zhu and Y. W. Zhu, Young, “Biometric personal identification system based on iris analysis,” American Patent, 1994.
[16] J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 11, pp. 1148–1161, 1993.
[17] D. M. Rankin, B. W. Scotney, P. J. Morrow, and B. K. Pierscionek, “Iris recognition—the need to recognise the iris as a dynamic biological system: Response to Daugman and Downing,” Pattern Recognit., vol. 46, no. 2, pp. 611–612, 2013.
[18] J. Daugman, “Iris Recognition,” Am. Sci., vol. 89, no. 4, p. 326, 2001.
[19] J. Daugman, “How Iris Recognition Works,” IEEE teansactions circuits Syst. video Technol., vol. 14, no. 1, pp. 715–739, 2004.
[20] R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proc. IEEE, vol. 85, no. 9, pp. 1348–1363, 1997.
[21] B. Jensen and B. Jensen, Iridology Simplified, 5th ed. California USA: Iridologists International, 2011.
[22] C. Parmar, P. Grossmann, J. Bussink, P. Lambin, and H. J. W. L. Aerts, “Machine Learning methods for Quantitative Radiomic Biomarkers,” Nat. Publ. Gr., pp. 1–11, 2015.
[23] M. De Marsico, A. Petrosino, and S. Ricciardi, “Iris Recognition through Machine Learning Techniques: a Survey,” Pattern Recognit. Lett., 2016.
[24] M. Fern and E. Cernadas, “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?,” J. Mach. Learn. Res., vol. 15, pp. 3133–3181, 2014.
[25] M. Kuhn, “Building Predictive Models in R Using the Caret Package,” J. Stat. Softw., vol. 28, no. 5, pp. 1–26, 2008.
[26] A. D. Wibawa and M. H. Purnomo, “Early detection on the condition of Pancreas organ as the cause of diabetes mellitus by real time iris image processing,” in IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS, 2006, pp. 1008–1010.
[27] K. Sivasankar, “FCM based Iris Image Analysis for Tissue Imbalance Stage Identification,” in International Conference on Emerging Trends in Science, Engineering and Technology, Proceedings, 2012, pp. 210–215.
[28] D. H. Hareva, B. B. Sitorus, S. Lukas, N. O. Suharta, C. Sciences, U. P. Harapan, and L. Karawaci, “Implementation of iridology application on smartphone,” in 7th ICTS, 2013, pp. 33–38.