Data Mining Approach for Early Identification of Diabetic Retinopathy Using Electronic Health Records

Mukhamad Hasim Iswanto

doi:10.55927/fjst.v5i8.146

Authors

Mukhamad Hasim Iswanto UIN Profesor Kiai Haji Saifuddin Zuhri Purwokerto

DOI:

https://doi.org/10.55927/fjst.v5i8.146

Keywords:

Data Mining, Diabetic Retinopathy, Electronic Health Records, Early Identification, Diabetes Mellitus.

Abstract

Diabetic retinopathy is a complication of diabetes that is at risk of causing visual impairment so it requires early identification based on clinical data. This study aims to identify patterns of association between patient characteristics, glucose levels, diabetes duration, blood pressure, and retinopathy conditions through electronic health records. The study used an exploratory descriptive design based on EHR secondary data with purposive sampling of 30 medical records of diabetic patients who met the study criteria. Data were collected through electronic medical record documentation and analyzed using data mining techniques to find meaningful patterns between clinical variables. Results showed that longer duration of diabetes, higher glucose levels, and high blood pressure were the dominant patterns in patients with indications of retinopathy. These findings suggest that data mining on electronic medical records has the potential to support early screening and clinical decision-making

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