A Review on Prediction of Diabetic Retinopathy Using Data Mining Algorithms

Authors(2) :-Kajal Sanjay Kothare, Prof. Kalpana Malpe

The risking components of diabetic retinopathy (DR) were examined broadly in the past investigations, yet it stays obscure which chance variables were more connected with the DR than others. On the off chance that we can distinguish the DR related hazard factors all the more precisely, we would then be able to practice early avoidance systems for diabetic retinopathy in the most high-chance populace. The motivation behind this examination to study and consider the different predicting mechanisms for the DR in diabetes mellitus utilizing data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.

Authors and Affiliations

Kajal Sanjay Kothare
M-Tech, Department of Computer Science and Engineering, Guru Nanak Institute of Engineering & Technology, Nagpur, Maharashtra, India
Prof. Kalpana Malpe
Assistant Professor Department of Computer Science and Technology, Guru Nanak Institute of Engineering & Technology, Nagpur, Maharashtra, India

Data Mining, Artificial neural fuzzy interference system, K-Nearest-Neighbor (KNN), Machine Learning (ML), Support Vector Machines, Decision Trees

  1. Chew EY, Klein ML, Ferris FL 3rd, Remaley NA, Murphy RP, Chantry K, Hoogwerf BJ, Miller D., "Association of elevated serum lipid levels with retinal hard exudate in diabetic retinopathy. Early treatment diabetic retinopathy study (ETDRS) report", 22. Arch Ophthalmol. 2016; 114(9):1079–84.
  2. American Diabetes Association. Standards of medical care in diabetes–2014. Diabetes Care. 2014;37(Suppl 1):S14–80.
  3. Fong DS, Aiello L, Gardner TW, King GL, Blankenship G, Cavallerano JD, Ferris FL 3rd, Klein R, "American Diabetes A. Retinopathy in diabetes. Diabetes Care", 2004;27(Suppl 1):S84–7.
  4. Kempen JH, O'Colmain BJ, Leske MC, Haffner SM, Klein R, Moss SE, Taylor HR, Hamman RF. "The prevalence of diabetic retinopathy among adults in the United States", Arch Ophthalmol. 2004; 122(4):552–63.
  5. Huang YY, Lin KD, Jiang YD, Chang CH, Chung CH, Chuang LM, Tai TY, Ho LT, Shin SJ. "Diabetes-related kidney, eye, and foot disease in Taiwan: an analysis of the nation wide data for 2000-2009", J Formos Med Assoc. 2012; 111(11):637–44.
  6. Early Treatment Diabetic Retinopathy Study Research Group. "Focal Photocoagulation treatment of diabetic macular edema. Relationship of treatment effect to fluorescein angiographic and other retinal characteristics at baseline: ETDRS report no. 19", Early treatment diabetic retinopathy study research group. Arch Ophthalmol. 1995; 113(9):1144–55.
  7. Chang TJ, Jiang YD, Chang CH, Chung CH, Yu NC, Chuang LM. Accountability, utilization and providers for diabetes management in Taiwan, 2000–2009: an analysis of the National Health Insurance database. J Formos Med Assoc. 2012;111(11):605–16. Tsao et al. BMC Bioinformatics 2018, 19(Suppl 9):283 Page 120 of 121
  8. Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, Chen SJ, Dekker JM, Fletcher A, Grauslund J, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012; 35(3):556–64.
  9. UK Pospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. UK Prospective Diabetes Study Group. BMJ. 1998; 317(7160):703–13.
  10. Kowall B, Rathmann W. HbA for diagnosis of type 2 diabetes. Is there an optimal cut point to assess high risk of diabetes complications, and how well does the 6.5% cut-off perform? Diabetes Metab Syndr Obes. 2013; 6:477–91.
  11. Hosseini SM, Maracy MR, Amini M, Baradaran HR. A risk score development for diabetic retinopathy screening in Isfahan-Iran. J Res Med Sci. 2009;14(2):105–10.
  12. Aspelund T, Thornorisdottir O, Olafsdottir E, Gudmundsdottir A, Einarsdottir AB, Mehlsen J, Einarsson S, Palsson O, Einarsson G, Bek T, et al. Individual risk assessment and information technology to optimise screening frequency for diabetic retinopathy. Diabetologia. 2011;54(10):2525–32.
  13. Semeraro F, Parrinello G, Cancarini A, Pasquini L, Zarra E, Cimino A, Cancarini G, Valentini U, Costagliola C. Predicting the risk of diabetic retinopathy in type 2 diabetic patients. J Diabetes Complicat. 2011;25(5):292–7.
  14. Dr. Karim Hashim Al-Saedi, Dr. Razi Jabur Al-Azawi, Rasha Asaad Kamil, - Design and Implementation System to Measure the Impact of Diabetic Retinopathy Using Data Mining Techniques, International Journal of Innovative Research in Electronics and Communications (IJIREC) Volume 4, Issue 1, 2017, PP 1-6
  15. Abhilash Bhaisare, Sagar Lachure, Amol Bhagat, Jaykumar Lachure - Diabetic Retinopathy Diagnosis Using Image Mining, International Research Journal of Engineering and Technology (IRJET), Volume: 03, Issue: 10, Oct -2016
  16. K. R. Ananthapadmanaban and G. Parthiban. - Prediction of Chances - Diabetic Retinopathy using Data Mining Classification Techniques. Indian Journal of Science and Technology, Vol 7(10), 1498–1503, October 2014
  17. M. Usman Akram, Shehzad Khalid, Shoab A. Khan - Identification and classification of microaneurysms for early detection of diabetic retinopathy, Pattern Recognition, Vol. 46, No. 1, 2013, pp. 107–116.
  18. Vimala Balakrishnan , Mohammad R. Shakouri and Hooman Hoodeh - Integrating association rules and case-based reasoning to predict retinopathy, Maejo Int. J. Sci. Technol. 2012, 6(03), 334-343, ISSN 1905-7873
  19. M. Tamilarasi and Dr. K. Duraiswamy -A Survey for Automatic Detection of Non- Proliferative Diabetic Retinopathy, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 1, January 2014
  20. Ramon Casanova , Santiago Saldana, Emily Y. Chew, Ronald P. Danis, Craig M. Greven, Walter T. Ambrosius - Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses, Published: June 18, 2014https://doi.org/10.1371/journal.pone.0098587
  21. S. Sagar Imambi and T. Sudha - Building Classification System to Predict Risk factors of Diabetic Retinopathy Using Text mining, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2309-2312
  22. Hayrettin Evirgen, Menduh Çerkezi- Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique,ijaedu.ocerintjournals.org/tojsat/issue/22642/241874, Sep, 2014

Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 266-272
Manuscript Number : CSEIT195179
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Kajal Sanjay Kothare, Prof. Kalpana Malpe, "A Review on Prediction of Diabetic Retinopathy Using Data Mining Algorithms", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.266-272, January-February-2019.
Journal URL : http://ijsrcseit.com/CSEIT195179

Article Preview