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Comparing Naive Bayes and Decision Tree Techniques for Predicting the Risk of Diabetic Retinopathy

G. Parthiban, S.K. Srivatsa


Classifying data is a common task in Machine learning. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Diabetic retinopathy the most common diabetic eye disease, is caused by complications that occurs when blood vessels in the retina weakens or distracted. We have applied machine learning methods to predict the early detection of eye disease diabetic retinopathy and found that Decision Tree method to be 90% accurate. The performance was also measured by sensitivity and specificity.


Data Mining, Naïve Bayes Method, Decision Tree, Diabetes, Diabetic Retinopathy.

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Thuraisingham, B.(2000) “A Primer for Understanding and Applying Data Mining”, IT Professional, pp: 28-31.

World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: Available: http://www.who. int/ diabetes/en

Akara Sopharak, Bunyarit Uyyanonvara, Sarah barman (2011) ‘Automatic Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Mathematical Morphology Methods’, IAENG International Journal of Computer Science, IJCS_38_3_15

Lily Tapak & Hossein Mahjub,’ Real Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran’,Available from:

Neera Singh, Ramesh Chandra Tripathi (2010), ‘Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques’, International Journal of Computer Applications (0975-8887), volume 8, No.2.

Jianchao Han, Juan Rodriguze & Mohsen Beheshti (2008), ‘Diabetes Data Analysis and Prediction Model Discovery Using Rapid Miner’, In Proceedings of the 2nd International Conference on Future Generation Communication and Networking, vol.3, pp. 96-99

Sellappan Palaniappan & Rafiah Awang (2008), ‘Intelligent Heart disease Prediction System using Data Mining Techniques’, International Journal of Computer Science and Network Security, vol.8, no. 8, pp. 343-350

Sarojini Balakrishnan, Ramaraj Narayanaswamy. “An Empirical Study on the Performance of Integrated Hybrid Prediction Model on the Medical Datasets”. International Journal of Computer Applications 29(5):1-6, September 2011. Published by Foundation of Computer Science, New York, USA. ISBN: 978-93-80864-76-4.

Lily Tapak, Hossein Mahjub,Omid Hamidi, & Jalal Poorolajal (2013), ‘Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran’, Healthc Inform Res. Vol 19, pp.177–185

Vallabha,D., Dorairaj, R., Namuduri K. and Thompson., H, "Automated Detection and Classification of Vascular Abnormalities in Diabetic Retinopathy", IEEE, 2004.

Cho, H. Y., Lee, D. H., Chung, S. E., & Kang, S. W. (2010). Diabetic Retinopathy and Peripapillary Retinal Thickness. Korean J Opthalmol, 24(1), 16-22

Sivakumari K, Flora Mary Cyril Rathinabai A, Kaleena P.K, Jayaprakash P & Srikanth R (2010), ‘Molecular docking study of bark-derived components of Cinnamomum cassia on aldose reductase’, Indian Journal of Science and Technology, Vol.3, .No.8, pp. 1081-1088.

Parimalavalli R & Radhaisri S (2011), ‘Glycaemic index of stevia product and its efficacy on blood glucose level in type 2 diabetes’, , Indian Journal of Science and Technology, Vol.4, .No.3, pp. 318-321.

Akkarapol Sa-ngasoongsong & Jongsawas Chongwatpol (2012), ‘An Analysis of Diabetes Risk Factors Using Data Mining Approach’, pp. 1-11

Salim Diwani, ―Overview Applications of Data Mining In Health Care: The Case Study of Arusha Region‖, International Journal of Computational Engineering Research, Vol 03, Issue, 8, August 2013

Han KamberJ, M. 2006. Data Mining: Concepts and Techniques, 2nd ed. San Francisco: Morgan Kaufman.

Fayyad U, .Piatetsky-Shapiro, and .Smyth P (1996), “From Data Mining to Knowledge Discovery in Databases”, AI Magazine, Vol.17, pp.37-54.

Statistica tool,

Rapid Miner, ‘Machine learning software getting started’

Naïve bayes classifier based on applying bayes theorem: bayes classifier

Naïve Bayes Classifier, ‘Bayes theorem’

Sudha A, Gayathri P, N.Jaisankar, “Effective Analysis and Predictive Model of Stroke Disease using Classification Methods”, International Journal of Computer Applications (IJCA) Volume 43-No.14, April 2012, 0975-8887.


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