Iridology based diagnosis of kidney abnormalities due to diabetes mellitus
- N. Padmasini, Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, India, Corresponding author; E-mail: firstname.lastname@example.org
- J. Aarthi, U. Deepika, Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, India
- R. Deepshikhaa, Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, India
Iridology is a very useful technique to diagnose the abnormalities in various parts of our human body. The human iris is connected to all parts of the body through nerve strands. Iridologists see the eyes as windows into the body's state of health through iris images. Iridologists claim they can use the iridology charts to distinguish between healthy organs and those that are overactive, inflamed, or distressed through the change in pigmentation or due to lacunae formation in the various iris regions. As the diabetic population is increasing day by day, it is crucial to detect early changes in the kidneys due to Type 1 or Type 2 diabetes, in order to avoid kidney failure. This work aims to diagnose the presence of any abnormality in the kidney due to diabetes mellitus using iris images as well as iridology charts and hence, automatically categorizing normal and abnormal cases using the Random Forest Classification algorithm. Through this algorithm 83.33 percent accuracy is achieved. As a pilot study twenty four normal and abnormal images were taken and analyzed. However, more images are to be analysed to claim iridology as a tool for diagnosing kidney ailments.
Asuntha, A., Siddartha, M., Udhaykumar, K., & Srinivasan, A. (2019). Identification of diabetics mellitus using Iridology. International Journal of Research in Pharmaceutical Sciences, 10(3), 1821-1823. DOI: https://doi.org/10.26452/ijrps.v10i3.1325
Ayda, Y. R., Mooventhan, A., Vinothkumar, M. H., Kayelarasi, C. A., Vijaykanth, S., & Manavalan, N. (2021). Comparative study on iris manifestations between COVID-19 severe cases and non-COVID individuals. Journal of Complementary and Alternative Medical Research, 14(1), 53-60. DOI: 10.9734/jocamr/2021/v14i130238
Amerifar, S., Targhi, A. T., & Dehshibi, M. M. (2015, October). Iris the picture of health: Towards medical diagnosis of diseases based on iris pattern. In 2015 Tenth IEEE. International Conference on Digital Information Management (ICDIM) (pp. 120-123). IEEE. DOI:10.1109/ICDIM.2015.7381861
Bansal, A., Agarwal, R., & Sharma, R. K. (2015). Determining diabetes using iris recognition system. International journal of diabetes in developing countries, 35(4), 432-438. DOI: 10.1007/s13410-015-0296-1
Chaskar, U. M., & Sutaone, M. S. (2012). Learning to predict diabetes from iris image analysis. International Journal of Biomedical Engineering and Technology, 9(1), 88-99. DOI: 10.1504/IJBET.2012.047373
Hiremath, B., Vajrala, N., Balakrishnan, R., Yeshwanth, N., & Jayantha, B. S. (2018, May). Identification of efficient features for detection of diabetes through iris patterns. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, (RTEICT) (pp. 2258-2263). IEEE. DOI: 10.1109/RTEICT42901.2018.9012374
Hussein, S. E., Hassan, O. A., & Granat, M. H. (2013). Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks. Biomedical Signal Processing and Control, 8(6), 534-541. DOI: 10.1016/j.bspc.2013.04.006
Jensen, B. (2012). Iridology simplified. Oxford , UK: Book Publishing Company.
Jogi, S. P., & Sharma, B. B. (2014, November). Methodology of iris image analysis for clinical diagnosis. In 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom) (pp. 235-240). IEEE. DOI:10.1109/MEDCOM.2014.7006010
Othman, Z., & Prabuwono, A. S. (2010, November). Preliminary study on iris recognition system: Tissues of body organs in iridology. In 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) (pp. 115-119). IEEE. DOI:10.1109/IECBES.2010.5742211
Padmasini, N., Umamaheswari, R., Kalpana, R., & Sikkandar, M. Y. (2020). Comparative study of iris and retinal images for early detection of diabetic mellitus. Journal of Medical Imaging and Health Informatics, 10(2), 316-325. DOI: https://doi.org/10.1166/jmihi.2020.2973
Prayitno, A., Wibawa, A. D., & Purnomo, M. H. (2016, October). Early detection study of Kidney Organ Complication caused by Diabetes Mellitus using iris image color constancy. In 2016 International Conference on Information & Communication Technology and Systems (ICTS) (pp. 146-149). IEEE. DOI: 10.3390/sym12122066
Samant, P., & Agarwal, R. (2018). Machine learning techniques for medical diagnosis of diabetes using iris images. Computer methods and programs in biomedicine, 157, 121-128.
Shampine, L. F., & Reichelt, M. W. (1997). The matlab ode suite. SIAM journal on scientific computing, 18(1), 1-22.
Wibawa, A. D., & Purnomo, M. H. (2006, December). Early detection on the condition of pancreas organ as the cause of diabetes mellitus by real time iris image processing. In APCCAS 2006-2006. IEEE Asia Pacific Conference on Circuits and Systems (pp. 1008-1010). IEEEDOI:10.1109/APCCAS.2006.342258