Document Type : Original Article

Authors

Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Introduction: Diabetes is one of the most common chronic diseases in the world. Incidence and
prevalence of diabetes are increasing in developing countries as well as in Iran. Retinopathy is
the most common chronic disorder in diabetic patients. Materials and Methods: In this study,
we used the information of diabetic patients’ reports that refer to endocrine and metabolism
research center of Isfahan University of Medical Sciences to determine diabetic retinopathy
risk factors. We used factor analysis to extract retinopathy’s factors. Factor analysis is using
to analyze multivariate data, in which a large number of dependent variables summarize into
the fewer independent factors. Factor analysis is applied, in both diabetic and nondiabetic
patients, separately. To investigate the efficacy of factor analysis, we used discriminant analysis.
Results: We investigated 3535 diabetic patients whose prevalence of retinopathy was 53.4%.
Six factors were extracted in each group (i.e. diabetic and nondiabetic groups). These six
factors were explained 69.5% and 69.6% of total variance in diabetic and nondiabetic groups,
respectively. Using original variables such as sex, weight, blood sugar control method, and
some laboratory variables, the correct classification rate of discriminant analysis was identified
as 67.4%. However, it decreased to 49.5% by using extracted factors. Discussion: Retinopathy
is one of the important disorders in diabetic patients that involves a large number of variables
and can affect its incidence. By the method of factor analysis, we summarize diabetic retinopathy
risk factors. Factor analysis is applied separately, in two diabetic and nondiabetic group. In
this way, 10 variables were summarized into the six factors. Discriminant analysis was used to
investigate the efficacy of factor analysis. Conclusion: Although factor analysis is a powerful
way to reduce the number of variables, in this study did not worked very well.

Keywords

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