##plugins.themes.academic_pro.article.sidebar##

Download
pdf
Statistic
Read Counter : 78 Download : 67

##plugins.themes.academic_pro.article.main##

Abstract

As technology progresses, data grows exponentially and is produced quickly from a wide variety of sources. Data storage, analysis, and interpretation become challenging due to data's variety and complexity. Despite the fact that we can now store such enormous amounts of data, much progress has to be made in terms of analyzing and making sense of that data. Better clinical routes to detect and predict diabetes at early stages are essential for reducing complications and delaying the onset of diabetes. The worldwide rise in the prevalence of diabetes is one of the most concerning trends in modern medicine. A high blood glucose level, caused by insufficient insulin synthesis or improper insulin response by body cells, characterizes the metabolic illnesses known together as diabetes mellitus. Organs as diverse as the brain, nervous system, heart, kidney, eyes, skin, and limbs may all suffer harm from diabetes mellitus. In this study, we used machine learning to differentiate between type 1 and type 2 diabetes trends

Keywords

analysis techniques classification machine learning

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Ban Hamid Ali, & Dr. Mahmoud koabaz. (2023). Developing a ML-Based Model for Detecting Diabetes. Texas Journal of Engineering and Technology, 23, 1–10. Retrieved from https://zienjournals.com/index.php/tjet/article/view/4281

References

  1. “International federation of diabetes,” https://www.idf.org, accessed: 201901-12.
  2. T. Y. Wong, J. Sun, R. Kawasaki, P. Ruamviboonsuk, N. Gupta, V. C. Lansingh, M. Maia, W. Mathenge,
  3. S. Moreker, M. M. Muqit et al., “Guidelines on diabetic eye care: The international council of ophthalmology
  4. recommendations for screening, follow-up, referral, and treatment based on resource settings,”
  5. Ophthalmology.
  6. S. Lin, P. Ramulu, E. L. Lamoureux, and C. Sabanayagam, “Addressing risk factors, screening, and
  7. preventative treatment for diabetic retinopathy in developing countries: a review,” Clinical & experimental
  8. ophthalmology, vol. 44, no. 4, pp. 300–320, 2016.
  9. M. W. Stewart, “Treatment of diabetic retinopathy: recent advances and unresolved challenges,” World
  10. journal of diabetes, vol. 7, no. 16, p. 333, 2016.
  11. T. Das, R. n, K. Ramasamy, and P. K. Rani, “Telemedicine in diabetic retinopathy: current status and
  12. future directions,” Middle East African journal of ophthalmology, vol. 22, no. 2, p. 174, 2015.
  13. M. Abra`moff, J. Reinhardt, S. Russell, J. Folk, V. Mahajan, M. Niemeijer, and G. Quellec, “Automated
  14. early detection of diabetic retinopathy,” Ophthalmology, vol. 117, no. 6, pp. 1147–1154, 2010.
  15. D. DeBuc, “A review of algorithms for segmentation of retinal image data using optical coherence
  16. tomography,” Image Segmentation,”(InTech, 2011), 2011.
  17. “Diabetic retinopathy,” https://www.aoa.org, 2019, accessed: 2019-01-22.
  18. W. Bu, X. Wu, X. Chen, B. Dai, and Y. Zheng, “Hierarchical detection of hard exudates in color retinal
  19. images,” Journal of Software, vol. 8, no. 11, pp. 2723–2732, 2013.
  20. K. M. Adal, P. G. Van Etten, J. P. Martinez, K. W. Rouwen, K. A. Vermeer, and L. J. van Vliet, “An
  21. automated system for the detection and classification of retinal changes due to red lesions in longitudinal
  22. fundus images,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 6, pp. 1382–1390, 2018.
  23. A. Sopharak, B. Uyyanonvara, and S. Barman, “Automated microaneurysm detection algorithms applied
  24. to diabetic retinopathy retinal images,” Maejo International Journal of Science and Technology, vol. 7, no. 2,
  25. p. 294, 2013.