Probability based approach for predicting the course of disease in diabetic retinopathy patients 


Ramesh Chandra Tripathi, Neera Singh*


Bioinformatics Department, Indian Institute of Information Technology, Allahabad

Email * Corresponding author

Article Type




Received July 21, 2010; Accepted September 12, 2010; Published November 1, 2010


The number of Diabetes patients has risen in both the developing and the developed nations. It is associated with lot complications retinopathy, nephropathy, neuropathy etc. Diabetic retinopathy is one of the leading causes of preventable blindness. Diabetic patients have to be monitored at regular intervals to detect any signs of retinopathy and deterioration of vision and timely intervention. This requires lot of time and cost both on the part of the patient and the specialist. Therefore there is a need to differentiate the ‘high risk’ patients from the ‘low risk’ patients, so that the high risk ones can be managed more rigorously while the low risk patients can be referred for less frequent screenings and checkups. Data of around 100 patients with Grade 1 retinopathy was collected. Their physiological parameters with their DR grading after 3 years was recorded. Physiological parameters which were having a higher impact on the course of Retinopathy were taken (e.g. Mild blood urea, Hypertension and Smoking in this case). Transition probabilities of going from one stage to other were calculated. Probability of having a single physiological parameter in a given stage of DR at a given point of time was calculated. Probability of various combinations of these physiological parameters in a given stage of disease was calculated. Then by knowing the present stage of that disease future stage (3 years later in this case) of the disease can be predicted. Based on these predictions, the ‘high risk’ patients are differentiated from the ‘low risk’ patients and are accordingly referred for screenings and interventions.



Diabetic Retinopathy (DR), Probability, physiological parameters, prediction, high risk


Tripathi et al. Bioinformation, 5 (5) 198-201, 2010

Edited by

P. Kangueane






Biomedical Informatics



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