The Risk Cluster in Type 2 Diabetes Mellitus Based on Risk Parameters Using Fuzzy C-Means Algorithm

Abstract

The prevalence of type 2 diabetes mellitus increases every year. In the long term, type 2 diabetes mellitus can lead to complications of other diseases. This study aimed to analyze the risk cluster for type 2 diabetes mellitus based on risk parameters using the Fuzzy C-Means algorithm. The benefit of analyzing the risk cluster as an initial screening to prevent the occurrence of type 2 diabetes mellitus. This study used 905 subjects’ data consisting of 562 males and 343 females. After the data preprocessing, the optimal number of clusters was determined using a Fuzzy C-Means algorithm process. Subsequently, the Pearson correlation test was conducted to determine the correlation between the risk parameters of type 2 diabetes mellitus and the cluster results. The study resulted in 2 risk clusters, subjects in cluster 1 were older than 60 years (34.1%), had a family history of type 2 diabetes mellitus (62.7%), had hypertension (55.4%), routinely took medicines (73.5%), undertook physical activity for less than half an hour (40.5%), and had a high blood pressure level (53.5%). The Pearson correlation test found that age, regular medication use, hypertension and blood pressure level all seem to have significant correlations with cluster outcomes. The risk cluster of type 2 diabetes mellitus was separated into two clusters using Fuzzy C-Means algorithm, namely the high-risk cluster and the low-risk cluster.

Keywords

• type 2 diabetes mellitus
• cluster analysis
• fuzzy c-means algorithm
• pearson correlation