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Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique

Year 2014, Volume: 4 Issue: 3, 32 - 37, 23.07.2016

Hayrettin Evirgen Menduh Çerkezi

Abstract

Diabetic retinopathy is the most common form of eye problem affecting people with diabetes, usually only affects people who have had diabetes for a long time period and can result in blindness. The aim of this study is to examine the naive Bayes algorithm which is one of the classification methods in data mining, and to analyze real life dataset in order to built predictive system for diabetic retinopathy disease. A total of 385 diabetes patients’ data were used to train the prediction system. All the categorical features in the dataset were selected by doctors and evaluation was made based on these features. The dataset was obtained at the Eye Clinic of the Sakarya University Educational and Research Hospital. It has been proven with cross-validation that naive Bayes algorithm can be used for diabetic retinopathy prediction with an improved accuracy of 89%

Keywords

Naive Bayes Diabetic Retinopathy Data Mining

References

  • Han, J. & Kamber, M. (2006). Data Mining Concepts and Techniques, Morgan Kaufman Publishers.
  • Liao, S.-C. and Lee, I.-N. (2002). Appropriate medical data categorization for data mining techniques, MED. INFORM., Vol. 27, no. 1, 59-67.
  • Fang, X. (2009). Are You Becoming a Diabetic? A Data Mining Approach, Sixth International Conference on Fuzzy Systems and Knowledge Discovery.
  • Salehi, M., Parandeh N.M., Soltain Sarvestani, A. & Savafi A.A. (2010). Predictind Breast Cancer Survivability Using Data Mining Techniques, 2nd Internetional Conference on Software Technology and Engineering (ICSTE).
  • Shouman, M., Turner, T. & Stocker, R. (2012). Using Data Mining Techniques in Heart Disease Diagnosis and Treatment, Japan-Egypt Conference on Electronics, Communication and Computers.
  • Balakrishnan, V., Shakouri, M. R., Hoodeh, H. & Hakso-Soo, L. (2012). Predictions Using Data Mining and Casebased Reasoning: A Case Study for Retinopathy, World Academy of Science and Technology 63.
  • Klein, R., Klein, B.E.K., Moss, S.E., Wong, T.Y., Hubbard, L., Cruickshanks, K.J. & Palta, M. (2004). The Relation of Retinal Vessel Caliber to the Incidence and Progression of Diabetic Retinopathy: XIX: The Wisconsin Epidemiologic Study of Diabetic Retinopathy, Archives of Ophthalmology, vol. 122, pp. 76-83.
  • Chan, Ch-L., Liu, Y.Ch. & Luo, Sh-H. (2008). Investigation of Diabetic Microvascular Complications Using Data Mining Techniques, International Joint Conference on Neural Networks (IJCNN 2008)

Year 2014, Volume: 4 Issue: 3, 32 - 37, 23.07.2016

Hayrettin Evirgen Menduh Çerkezi

Abstract

References

  • Han, J. & Kamber, M. (2006). Data Mining Concepts and Techniques, Morgan Kaufman Publishers.
  • Liao, S.-C. and Lee, I.-N. (2002). Appropriate medical data categorization for data mining techniques, MED. INFORM., Vol. 27, no. 1, 59-67.
  • Fang, X. (2009). Are You Becoming a Diabetic? A Data Mining Approach, Sixth International Conference on Fuzzy Systems and Knowledge Discovery.
  • Salehi, M., Parandeh N.M., Soltain Sarvestani, A. & Savafi A.A. (2010). Predictind Breast Cancer Survivability Using Data Mining Techniques, 2nd Internetional Conference on Software Technology and Engineering (ICSTE).
  • Shouman, M., Turner, T. & Stocker, R. (2012). Using Data Mining Techniques in Heart Disease Diagnosis and Treatment, Japan-Egypt Conference on Electronics, Communication and Computers.
  • Balakrishnan, V., Shakouri, M. R., Hoodeh, H. & Hakso-Soo, L. (2012). Predictions Using Data Mining and Casebased Reasoning: A Case Study for Retinopathy, World Academy of Science and Technology 63.
  • Klein, R., Klein, B.E.K., Moss, S.E., Wong, T.Y., Hubbard, L., Cruickshanks, K.J. & Palta, M. (2004). The Relation of Retinal Vessel Caliber to the Incidence and Progression of Diabetic Retinopathy: XIX: The Wisconsin Epidemiologic Study of Diabetic Retinopathy, Archives of Ophthalmology, vol. 122, pp. 76-83.
  • Chan, Ch-L., Liu, Y.Ch. & Luo, Sh-H. (2008). Investigation of Diabetic Microvascular Complications Using Data Mining Techniques, International Joint Conference on Neural Networks (IJCNN 2008)
There are 8 citations in total.

Details

Other ID JA56FV84RR
Journal Section Articles
Authors

Hayrettin Evirgen This is me

Menduh Çerkezi This is me

Publication Date July 23, 2016
Published in Issue Year 2014 Volume: 4 Issue: 3

Cite

APA Evirgen, H., & Çerkezi, M. (2016). Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique. TOJSAT, 4(3), 32-37.
AMA Evirgen H, Çerkezi M. Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique. TOJSAT. July 2016;4(3):32-37.
Chicago Evirgen, Hayrettin, and Menduh Çerkezi. “Prediction and Diagnosis of Diabetic Retinopathy Using Data Mining Technique”. TOJSAT 4, no. 3 (July 2016): 32-37.
EndNote Evirgen H, Çerkezi M (July 1, 2016) Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique. TOJSAT 4 3 32–37.
IEEE H. Evirgen and M. Çerkezi, “Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique”, TOJSAT, vol. 4, no. 3, pp. 32–37, 2016.
ISNAD Evirgen, Hayrettin - Çerkezi, Menduh. “Prediction and Diagnosis of Diabetic Retinopathy Using Data Mining Technique”. TOJSAT 4/3 (July 2016), 32-37.
JAMA Evirgen H, Çerkezi M. Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique. TOJSAT. 2016;4:32–37.
MLA Evirgen, Hayrettin and Menduh Çerkezi. “Prediction and Diagnosis of Diabetic Retinopathy Using Data Mining Technique”. TOJSAT, vol. 4, no. 3, 2016, pp. 32-37.
Vancouver Evirgen H, Çerkezi M. Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique. TOJSAT. 2016;4(3):32-7.