PREDICTING RETINOPATHY RISK AMONG DIABETIC PATIENTS: A DATA MINING APPROACH

Abstract

In medicine, data collection plays an important role in the diagnosis of diseases and treatment of patients. Physicians have to cope with a large amount of patient-related data, and they often have to review the patient’s whole history or other similar cases. Data is mainly collected to find out if there are related patterns and results that can shed light on the nature of the investigated disease. Mechanized data mining has tremendous value in the diagnosis and treatment of diseases, and can be especially helpful in the diagnosis and treatment of diabetes, a disease that inflicts a large portion of the population. Now, diabetes is the fourth cause of mortality among the general population in developing countries. Retinopathy is a chronic complication of diabetes that has serious consequences including blindness if not diagnosed as early as possible. This study uses a sample of 310 Diabetic patients, half of them have the diagnosis of retinopathy ([Formula: see text]) and investigates 29 variables including age, gender, HbA1c, treatment type and etc. Our results indicate that Decorate algorithm in Weka software is the most vigorous algorithm for the purpose of the study with an accuracy rate of 0.86. The study also investigates efficacy criteria related to databases and risk factors related to this disease including age, duration of the disease, BMI, HDL level, HbA1c, FBS, 2HPPG, blood pressure, and treatment method.