Identification of Diabetes Mellitus Risk in Women using Random Forest

Eka Wijaya

doi:10.63017/jdsi.v3i1.95

Authors

Eka Wijaya Department of Informatics Engineering, Institut Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Riau, Indonesia

DOI:

https://doi.org/10.63017/jdsi.v3i1.95

Keywords:

Diabetes, Algorithm, Classification, Women, Random Forest

Abstract

Diabetes Mellitus (DM) is one of the chronic diseases that can cause various serious complications, especially in women. Early risk identification is an important step in preventing the progression of this disease. This study aims to identify the factors influencing the risk of diabetes in women by analyzing data from several parameters, namely the number of pregnancies, glucose level, blood pressure, skin thickness, insulin level, body mass index (BMI), diabetes pedigree function, and age. A quantitative approach was used in this study with descriptive and inferential statistical analysis methods. The research results show that glucose levels and BMI are the most significant factors in increasing the risk of diabetes, followed by family history of diabetes and age. In addition, the number of pregnancies also has an impact on the risk of diabetes, especially in women with a history of gestational diabetes. This research concludes that the combination of several parameters can be used to predict the risk of diabetes more accurately, especially in women. These results are expected to support early prevention efforts and better clinical decision-making in the management of diabetes.

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