Identifying Stratifications of Cancer Patient Visits: Approach of Clustering Using PCA of Mixed Data

Abstract

Cancer is a significant contributor to the burden of non-communicable diseases and one of the diseases with the highest costs in Indonesia’s health insurance system. Understanding key factors influencing cancer patient visits and risk groups under national health insurance supports evidence-based and sustainable cancer care financing. The aim is to identify key factors influencing inpatient visits among cancer survivors and map risk patterns to improve cancer health service policies, using a 1% sample of claim data from the national health insurance (JKN) program. The PCA of mixed data analysis revealed that cost-severity level and contributionward classes shared influence of the visits. After PCA, K-Means was applied and 4 clusters were obtained. K-Means can give better understanding of the patient visits, especially the need for distinct strategies to be implemented for the groups so that the burden of cancer disease financing under the national health insurance program can be reduced.