Discovering Breast Cancer Comorbidity: A Network Analysis - Community Detection Approach

In 2020, breast cancer was the most common type of new cancer cases and caused the death of 0.69 million people worldwide. As it is famous and lethal, this paper aims to discover the comorbidity of breast cancer using community detection algorithms, namely Belief, Girvan-Newman, and Spinglass to prevent further impacts. Four centrality algorithms were used to get the comorbidity. As a result, the Wang algorithm with a threshold = 0.5 is chosen to build the network. The significant comorbidities agreed by all three algorithms are the disease of mental health, gastrointestinal system disease, cardiovascular system disease, nervous system disease, lung disease, and lower respiratory tract disease. There are also four other comorbidities agreed by the two algorithms and eight comorbidities discovered by only the Spinglass algorithm. From the list of 12 breast cancer comorbidity diseases by the Danish National Patient Register (DNPR), all of the comorbidities’ causes led to our list of comorbidities, which is the rationale for the excellent result of this research.