Predictions Using Data Mining and Case-based Reasoning: A Case Study for Retinopathy
Diabetes is one of the high prevalence diseases
worldwide with increased number of complications, with retinopathy
as one of the most common one. This paper describes how data
mining and case-based reasoning were integrated to predict
retinopathy prevalence among diabetes patients in Malaysia. The
knowledge base required was built after literature reviews and
interviews with medical experts. A total of 140 diabetes patients- data
were used to train the prediction system. A voting mechanism selects
the best prediction results from the two techniques used. It has been
successfully proven that both data mining and case-based reasoning
can be used for retinopathy prediction with an improved accuracy of
85%.
[1] P. Zimmet, K.G.M. Alberti, and J. Shaw, "Global and societal
implications of the diabetes epidemic," Nature, vol. 414, pp. 782-787,
2001.
[2] P. P. Goh, "Status of Diabetic Retinopathy Among Diabetics Registered
to the Diabetic Eye Registry, National Eye Database, 2007," Med. J.
Malaysia, vol. 63, pp. 24-28, 2008.
[3] H.Y. Cho, D.H. Lee, S.E. Chung, and S.W. Kang, "Diabetic Retinopathy
and Peripapillary Retinal Thickness," Korean J Ophthalmol, vol. 24, pp.
16-22, 2010.
[4] B.N. Conway, R.G. Miller, and R. Klein, T.J. Orchard, "Prediction of
Proliferative Diabetic Retinopathy With Hemoglobin Level," Arch
Ophthalmol, vol. 127, pp. 1494-1499, 2009.
[5] R. Klein, B.E.K. Klein, S.E. Moss, T.Y. Wong, L. Hubbard, K.J.
Cruickshanks, and M. Palta, "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, 2004.
[6] N. Cheung, S.L. Rogers, K.C. Donaghue, A.J. Jenkins, G. Tikellis, and
T.Y. Wong, "Retinal Arteriolar Dilation Predicts Retinopathy in
Adolescents with Type 1 Diabetes," Diabetes Care, vol. 31, pp. 1842-
1846, 2008.
[7] T.T. Nguyen, J. Wang, A. Sharrett, F. Islam, R. Klein, K. Klein, M.
Cotch, and T. Wong, "Relationship of Retinal Vascular Caliber With
Diabetes and Retinopathy," Diabetes Care, vol. 31, pp. 544-549, 2008.
[8] C.L. Chan, Y.C. Liu, and S.H. Luo, "Investigation of diabetic
microvascular complications using data mining techniques," in Neural
Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational
Intelligence). IEEE International Joint Conference on Neural Networks,
2008, pp. 830-834.
[9] J. L. Kolodner, "An introduction to case-based reasoning," Artificial
Intelligence Review, vol. 6, pp. 3-34, 1992.
[1] P. Zimmet, K.G.M. Alberti, and J. Shaw, "Global and societal
implications of the diabetes epidemic," Nature, vol. 414, pp. 782-787,
2001.
[2] P. P. Goh, "Status of Diabetic Retinopathy Among Diabetics Registered
to the Diabetic Eye Registry, National Eye Database, 2007," Med. J.
Malaysia, vol. 63, pp. 24-28, 2008.
[3] H.Y. Cho, D.H. Lee, S.E. Chung, and S.W. Kang, "Diabetic Retinopathy
and Peripapillary Retinal Thickness," Korean J Ophthalmol, vol. 24, pp.
16-22, 2010.
[4] B.N. Conway, R.G. Miller, and R. Klein, T.J. Orchard, "Prediction of
Proliferative Diabetic Retinopathy With Hemoglobin Level," Arch
Ophthalmol, vol. 127, pp. 1494-1499, 2009.
[5] R. Klein, B.E.K. Klein, S.E. Moss, T.Y. Wong, L. Hubbard, K.J.
Cruickshanks, and M. Palta, "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, 2004.
[6] N. Cheung, S.L. Rogers, K.C. Donaghue, A.J. Jenkins, G. Tikellis, and
T.Y. Wong, "Retinal Arteriolar Dilation Predicts Retinopathy in
Adolescents with Type 1 Diabetes," Diabetes Care, vol. 31, pp. 1842-
1846, 2008.
[7] T.T. Nguyen, J. Wang, A. Sharrett, F. Islam, R. Klein, K. Klein, M.
Cotch, and T. Wong, "Relationship of Retinal Vascular Caliber With
Diabetes and Retinopathy," Diabetes Care, vol. 31, pp. 544-549, 2008.
[8] C.L. Chan, Y.C. Liu, and S.H. Luo, "Investigation of diabetic
microvascular complications using data mining techniques," in Neural
Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational
Intelligence). IEEE International Joint Conference on Neural Networks,
2008, pp. 830-834.
[9] J. L. Kolodner, "An introduction to case-based reasoning," Artificial
Intelligence Review, vol. 6, pp. 3-34, 1992.
@article{"International Journal of Medical, Medicine and Health Sciences:51036", author = "Vimala Balakrishnan and Mohammad R. Shakouri and Hooman Hoodeh and Loo and Huck-Soo", title = "Predictions Using Data Mining and Case-based Reasoning: A Case Study for Retinopathy", abstract = "Diabetes is one of the high prevalence diseases
worldwide with increased number of complications, with retinopathy
as one of the most common one. This paper describes how data
mining and case-based reasoning were integrated to predict
retinopathy prevalence among diabetes patients in Malaysia. The
knowledge base required was built after literature reviews and
interviews with medical experts. A total of 140 diabetes patients- data
were used to train the prediction system. A voting mechanism selects
the best prediction results from the two techniques used. It has been
successfully proven that both data mining and case-based reasoning
can be used for retinopathy prediction with an improved accuracy of
85%.", keywords = "Case-Based Reasoning, Data Mining, Prediction,
Retinopathy.", volume = "6", number = "3", pages = "47-4", }