CLUSTERIZATION OF DIABETES HEALTH INDICATORS WITH K-MEANS CLUSTER ALGORITHM - Yuda Perwira; Wira Apriani; Ahmad Zein; Sinta Lia Alfaris (Sean Institute, 2022)
Diabetes is one of the most common chronic diseases in the world, affecting millions of people every year and placing a significant financial burden on the economy. Diabetes is a serious chronic disease in which individuals lose the ability to effectively regulate blood glucose levels, and it can lead to decreased quality of life and life expectancy.The purpose of this study is to cluster diabetes health to be able to cluster quickly whether a person has diabetes, or prediabetes or is free from diabetes so that diabetes can be anticipated as early as possible. The data used in this study is the result of a survey from the US Behavioral Risk Factor Surveillance System (BRFSS) in 2015 which contains a net data collection of 253,680 survey responses to the CDC's 2015 BRFSS. The target variable Diabetes_012 has 3 classes. 0 for no diabetes or only during pregnancy, 1 for prediabetes, and 2 for diabetes.The method used in this study is the K-Means Clustering method where this method has been quite successful and is widely used by many researchers to cluster and predict, indicators of a person's diabetes health can be grouped into 3 groups, namely the health of people without diabetes, the health of people with prediabetes. and the health of people with type 2 diabetes, as for the results of the clustering of 2349 data, there are 235 people with health without diabetes, 1816 people with prediabetes health conditions and 298 people with type 2 diabetes.