Prediction and Diagnosis of Diabetic Retinopathy using Data Mining Technique

Yıl 2014, Cilt: 4 Sayı: 3, 32 - 37, 23.07.2016

Hayrettin Evirgen Menduh Çerkezi

Öz

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%

Anahtar Kelimeler

Naive Bayes, Diabetic Retinopathy, Data Mining

Kaynakça

• 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)