Cluster analysis of type II Diabetes Mellitus Patients with the Fuzzy C-means method - Simeftiany Indrilemta Lomo; Endang Darmawan; Sugiyarto (Annals of Mathematical Modeling, 2023)
Cluster analysis has been widely used in the fields of mathematics and health sciences. This study aims to classify distance-based data which are divided into several clusters. Accurate prediction from the outcome or survival rate of diabetic patients can be the key for the stratification of prognosis and therapy. A retrospective study of 447 medical record data of type II diabetes mellitus patients aged 18 years old or above and were hospitalized in the PKU Muhammadiyah Gamping Hospital from 2015-2019. Clustering is using the PCA-Fuzzy C-Means method based on patients’ survival status, demographic characteristics, therapy, and blood glucose (BG) levels. Clustering evaluation by Davies Bouldin Index (DBI). Data analysis is using Jupyter Notebook programme. Cluster formation are first cluster consists of 171 members, second cluster consists of 9 members, third cluster consists of 267 members with DBI 2,2645. 401 patients (89,7%) were recorded as alive and 46 patients (10,3%) were recorded as dead. A total of 447 patients: 54,1% were male; 90,6% were ≥ 45 years old; 66,4% has comorbidities; 51,7% had BG level of more than 200 mg/dl, and 57,7% received combination insulin+oral antidiabetic therapy.